Philippe Fournier-Viger

Distinguished professor, Shenzhen University

Publications

Below is the list of my books, chapters, journal papers, conference papers and workshop papers

Edited books

2021 1. Mehta, M., Pattel, M., Fournier-Viger, P., Lin, J. C.-W. (editors). Tracking and Preventing Diseases using Artificial Intelligence. Springer, 2021, ISBN: 978-3-030-76732-7 (book on SpringerLink)
2. Kiran, R. U., Fournier-Viger, P., Mondal, A., Luna, J.M., Lin, J. C.-W. (editors) Periodic Pattern Mining: Theory, Algorithms and Applications, Springer, 2021, ISBN: 978-981-16-3963-0 (book on SpringerLink)
2020 3.

Chiroma, H., Abdulhamid, S. M., Fournier-Viger, P., Garcia, N. M. (editors). Machine learning and Data Mining for Emerging Trends in Cyber Dynamics. Springer, 2021, iSBN: 978-3-030-66287-5 (book on SpringerLink)

2019 4. Fournier-Viger, P., Lin. J. C.-W., Vo, B., Nkambou, R., Tseng, V. S. (editors). High-Utility Pattern Mining: Theory, Algorithms and Applications, Springer, 2019. ISBN 978-3-030-04920-1 (book on SpringerLink)
Sample chapters (drafts);
high utility pattern mining book cyber dynamics book  tracking disease with artificial intelligence periodic

Book Chapters

2021 1. Nouioua, M., Fournier-Viger, P., He, G., Nouioua, F., Min, Z. (2020). A Survey of Machine Learning for Network Fault Management. In the book “Machine Learning and Data Mining for Emerging Trends in Cyber Dynamics”, Springer, p. 1-27. Corr. Author
2. Fournier-Viger, P., Wu. Y., Dinh, D.-T., Song, W., Lin, J.C.W. (2021). Discovering Periodic High Utility Itemsets in a Discrete Sequence. In the book “Periodic Pattern Mining: Theory, Algorithms and Application”, Springer, to appear. Corr. Author
3. Fournier-Viger, P., Chi, T. T., Wu, Y., Qu, J.-F., Lin, J. C.W., Li, Z. (2021). Finding Periodic Patterns in Multiple Discrete Sequences. In the book “Periodic Pattern Mining: Theory, Algorithms and Application”, Springer, to appear. Corr. Author
4. Ahmed, U., Lin, J.C.W., Fournier-Viger, P. (2021). Privacy Preservation of Periodic Frequent Patterns using Sensitive Inverse Frequency . In the book “Periodic Pattern Mining: Theory, Algorithms and Application”, Springer, to appear.
5. Wu, Y., Geng, M., Li, Y., Guo, L., Fournier-Viger. P. (2021). NetHAPP: High Average Utility Periodic Gapped Sequential Pattern Mining. In the book “Periodic Pattern Mining: Theory, Algorithms and Application”, Springer, to appear.
6. Lin, J. C.-W., Li, T., Fournier-Viger, P., Zhang, J. (2021). Analytics of Multiple-Threshold Model for High Average-Utilization Patterns in Smart City Environments. In the book Data-Driven Mining, Learning and Analytics for Secured Smart Cities, Springer.
2019 7. Fournier-Viger., P., Lin, J. C.-W., Truong, T., Nkambou, R. (2019). A survey of high utility itemset mining. In: Fournier-Viger et al. (eds). High-Utility Pattern Mining: Theory, Algorithms and Applications, Springer (to appear), p. 1-46. DOI: 10.1007/978-3-030-04921-8_1 Corr. Author
8. Qu, J.-F., Liu, M., Fournier-Viger (2019). Efficient algorithms for high utility itemset mining without candidate generation. In: Fournier-Viger et al. (eds). High-Utility Pattern Mining: Theory, Algorithms and Applications, Springer, p. 131-160. DOI: 10.1007/978-3-030-04921-8_5
9. Truong, T., Fournier-Viger., P. (2019). A survey of high utility sequential pattern mining. In: Fournier-Viger et al. (eds). High-Utility Pattern Mining: Theory, Algorithms and Applications, Springer, p. 97-130. DOI: 10.1007/978-3-030-04921-8_4
10. Dinh, D.-T., Huynh, V.-N., Le, B., Fournier-Viger, P., Huynh, U., Nguyen, Q.-M. (2019). A Survey of Privacy Preserving Utility Mining.In: Fournier-Viger et al. (eds). High-Utility Pattern Mining: Theory, Algorithms and Applications, Springer, p.207-232 DOI: 10.1007/978-3-030-04921-8_8
11. Djenouri, Y., Fournier-Viger, P., Belhadi, A., Lin, J. C.-W. (2019). Methaheuristics for High Utility and Frequent Itemset Mining . In: Fournier-Viger et al. (eds). High-Utility Pattern Mining: Theory, Algorithms and Applications, Springer, p. 261-278. DOI: 10.1007/978-3-030-04921-8_10
12. Wu. C.-W., Fournier-Viger, P., Gu, J. Y., Tseng, V.S. (2019). Mining Compact High Utility Itemsets without Candidate Generation. In: Fournier-Viger et al. (eds). High-Utility Pattern Mining: Theory, Algorithms and Applications, Springer. DOI: 10.1007/978-3-030-04921-8_11
2015 13. Snow, E., Moghrabi, C., Fournier-Viger, P. (2015). Automatic Grading of Open-ended Questions Using Semantic Web Ontologies and Functional Concepts. In. Taylor, K., Gerber, A., Meyer, T., Orgun, M. (Eds.) Ontologies: Theory and Applications in Information Systems and the Semantic Web, 12 pages.
2013 14. Faghihi, U., Fournier-Viger, P., Nkambou, R. (2013). CELTS: A Cognitive Tutoring Agent with Human-Like Learning Capabilities and Emotions. In Ayala, A. P. (Ed.) Intelligent and Adaptive Educational-Learning Systems: Achievements and Trends, Springer, pp. 339-365.1
<2010 15. Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E. (2010). Building Intelligent Tutoring Systems for Ill-Defined Domains. In Nkambou, R., Mizoguchi, R., Bourdeau, J. (Eds.). Advances in Intelligent Tutoring Systems, Springer, p.81-101. Corr. Author
16. Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E. (2010). Learning Procedural Knowledge from User Solutions To Ill-Defined Tasks in a Simulated Robotic Manipulator. In Romero et al. (Eds.). Handbook of Educational Data Mining, CRC Press, p. 451-465. Corr. Author
17. Fournier-Viger, P., Nkambou, R., Faghihi, U., Mephu Nguifo, E. (2009). Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies. In Cao, L. (Ed.) Data Mining and Multiagent Integration, Springer, p.77-92. Corr. Author

Journal Papers

2026. 1. Wen, M., Niu, X., Zhu, J., Fournier-Viger, P. (2026). Modeling Continuous and Heterogeneous Spatio-Temporal Dependencies for Accurate Traffic Forecasting. IEEE Internet of Things Journal. To appear
2. He, Y., Chen, Q., Wang, R., Fournier-Viger, P., Zhexue Huang, J. (2026). FreqMLNet: Non-transformer network with frequency domain reconstruction and multi-scale representation for time series forecasting. Neural Networks, 199: 108723.
2025. 3. Dinh, T., Hauchi, W., Fournier-Viger, P., Lisik, D., Ha, M.-Q., Dam, H-C., Huynh, V.-N. (2025). Categorial data clustering: 25 years beyond K-modes. Expert Systems with Applications (ESWA), Elsevier,272: 126608. DOI: 10.1016/j.eswa.2025.126608 EISCICCF-C
4. Nawaz, M. S., Fournier-Viger, P. Nawaz, S. Wu, Y., Song, W. (2025) HieRMVir: Interpretable Viral Classification via Hierarchical Deep Learning. IEEE Journal of Biomedical and Health Informatics (JBHI). to appear EISCICAS Q2JCR Q1Corr. AuthorIF: 7
5. Chang, L., Niu,X., Li, Z., Zhang, Z., Li, S., Fournier-Viger, P. (2025) ULDC: uncertainty-based learning for deep clustering. Applied Intelligence 55(3): 223. DOI: 10.1007/s10489-024-06125-2 EISCICAS Q3JCR Q2CCF-C
6. Nawaz, M. Z., Nawaz, M. S., Fournier-Viger, P., Nawaz, S., Lin, J. C.-W., Tseng, V.S. (2025). Efficient Genome Sequence Compression via the Fusion of MDL-Based Heuristics. Information Fusion. 120: 103083.
DOI: 10.1016/j.inffus.2025.103083		 EISCICAS Q1JCR Q1Corr. AuthorIF: 14.8
7. Nawaz, M. S., Fournier-Viger, P., Nawaz, M. Z., He, Y., Yun, U. (2025).HUF4WP: A Data-Fusion Framework Leveraging High-Utility Patterns for Renewable Energy Classification. Information Fusion. to appear
DOI:		 EISCICAS Q1JCR Q1Corr. AuthorIF: 14.8
8. Li, M., Niu, X., Zhu, J., Fournier-Viger, Wu, Y. (2025). STR: Spatio-temporal Trajectory Representation Learning with Dual-Focus Encoder for Whole Trajectory Similarity Computation. Information Fusion. volume 123, article 103231.
DOI: 10.1016/j.inffus.2025.103231		 EISCICAS Q1JCR Q1IF: 14.8
9. Chen, E., Nawaz, M. Z, Nawaz, M., Fournier-Viger, P., Sun, M. (2025). HMP: Efficient Heuristic Algorithms for MDL-Based Itemset Mining. Applied Soft Computing, Elsevier. 104: 107200
DOI: 10.1016/j.asoc.2021.107200		 EISCICAS Q2JCR Q1Corr. AuthorIF: 6.6
10. Nawaz, M. Z., Nawaz, M. S., Fournier-Viger, P. Selmaoui-Folcher, N. (2025) GRIMP: A Genetic Algorithm for Compression-based Descriptive Pattern Mining. Expert Systems, Wiley, 42(5)
DOI: 10.1111/exsy.70033		 EISCICAS Q4JCR Q2Corr. Author
11. Tung, N. T., Nguyen, T. D. D., Nguyen, L. T. T., Vu. D.-L., Fournier-Viger, P., Vo, B. (2025). Mining Cross-level High Utility Itemsets in Unstable and Negative Profit Databases. IEEE Transactions on Knowledge and Data Engineering (TKDE), 36(9):4874-4889.
DOI: 10.1109/TKDE.2023.3334300		 EISCICAS Q2JCR Q1CCF-A
12. Chen, Q., He, Y., Fournier-Viger, P. Huang, J. Z. (2025). Adaptive Frequency-Domain Feature Extraction with Large Language Models for Accurate Electricity Market Forecasting. IEEE Transactions on Consumer Electronics. 71(3): 9087-9101.
DOI: 10.1109/TCE.2025.3585889		 EISCICAS Q2JCR Q1IF: 8.6
13. He, Y., Ou, G., Fournier-Viger, P., Huang, J. Z. (2025). Attribute grouping-based naive Bayesian classifier. Science China Information Sciences, 68(3): 132106
DOI: 10.1007/s11432-022-3728-2		 EISCI
14. Zhang, M., Liu, G., Dai, S., Chen, J., Fournier-Viger, P. (2025). Large Deviation Algorithms for Thresholding Bandit Problem. Big Data Mining and Analytics (BDMA), Tsinghua University Press, to appear EICAS Q1IF: 13.6
15. Qian, S., He, H., Wu, H., Fournier-Viger, P., Li, H., Huang, S. (2025). A hybrid constrained multi-objective algorithm for dynamic economic emission dispatch. International Journal of Electrical Power and Energy Systems, Elsevier, to appear. SCICAS Q2JCR Q1IF: 4.6
16. Zheng, Y., Gan, W., Chen, Z., Zhou, P., Fournier-Viger, P. (2025). SeqRFM: Fast RFM Analysis in Sequence Data. Information Sciences, to appear... EISCI
17. Ou, G.-L., He, Y., Fournier-Viger, P., Huang, J. Z. (2025). A novel multi-source weighted naive Bayes classifier. Information sciences. 721: 122568 (2025)
DOI: 10.1016/j.ins.2025.122497		 EISCICAS Q2JCR Q1CCF-B
18. Wu. Y., Lou, S., Li, Y., Guo, L., Fournier-Viger, P., Wu., X. (2025). OUTO-Miner: Detecting outlying occurrences in maximal frequent order-preserving patterns in time series. Information Sciences. 720: 122497
DOI: 10.1016/j.ins.2025.122497		 EISCICAS Q2JCR Q1CCF-BIF: 5.3
19. Wu, Y. ... Fournier-Viger, P. (2025) CoTP-Miner: Co-occurrence Three-Way Sequential Pattern Mining. Knowledge-based Systems (KBS), 332: 114803 (2025) EISCI CAS Q2JCR Q1CCF-C
20. Chen, J-Q., He, Y., Cheng, Y., Fournier-Viger, P., Nagaratnam, P., Huang, J. Z. (2025). A Novel Flexible Kernel Density Estimator for Multimodal Probability Density Functions. CAAI Transactions on Intelligence Technology. 10(6): 1759-1
DOI: 10.1049/cit2.70063
21. Lan, D., Sun, C., Dong, X., Qiu, P., Gong, Y., Liu, X., Fournier-Viger, P., Zhang, C. (2025). .TK-RNSP: Efficient Top-K Repetitive Negative Sequential Pattern mining. Information processing and management (IPM). 62(3): 104077.
DOI: 10.1016/j.ipm.2025.104077		 EISCICAS Q1JCR Q1IF: 7.4
22. Lisik, D., Shah, S. A., Basna, R., Dinh, T., Browne, R., Andrews, J., Wallace, M., Ezugwu, A., Marusic, A., Tran, D., Torres-Sospedra, J., Dam, H.-C., Fournier-Viger, P., Hennig, C., Timmerman, M., Warrens, M. J., Ceulemans, E., Nwaru, B., Hernandez-Boussard, T. M. (2025). Protocol for development of a checklist and guideline for transparent reporting of cluster analyses (TRoCA). BMJ Open. BMJ Publishing Group, to appear EISCICAS Q1JCR Q1IF: 2.7
23. Li, Y., Wang, Z., Liu, J., Guo, L, Fournier-Viger, P, Wu, Y., Wu X. (2025). Mining Repetitive Negative Sequential Patterns with Gap Constraints. ACM Transactions on Knowledge Discovery from Data (TKDD), ACM, 19(4): 1-29.
DOI: 10.1145/3716390		 EISCICAS Q4JCR Q2IF: 3.9
2024. 24 Nawaz, Z., Nawaz, M. S., Fournier-Viger, P., He, Y. Analysis and Classification of Fake News using Sequential Pattern Mining, Big Data Mining and Analytics (BDMA), Tsinghua University Press, 7(3):942-963
DOI: 10.26599/BDMA.2024.9020015		 EICAS Q1Corr. AuthorIF: 13.6
25. Nawaz, M. S., Nawaz, M. Z., Fournier-Viger, P., Luna, J.-M. (2024). Analysis and Classification of Employee Attrition and Absenteeism in Industry: A Sequential Pattern Mining-Based Methodology. Computers in Industry (COMIND). Elsevier, August 2024, article 104106.
DOI: 10.1016/j.compind.2024.104106
 EISCICAS Q1JCR Q1Corr. AuthorIF: 10
26. Nawaz, M. S., Fournier-Viger, P., Nawaz, S., Gan, W., He, Y. (2024). FSP4HSP: Frequent sequential patterns for the improved classification of heat shock proteins, their families, and sub-types. International Journal of Biological Macromolecules (BIOMAC). Elsevier, Volume 277, Part 1, October 2024, article 134147.
DOI: 10.1016/j.ijbiomac.2024.134147		 EISCICAS Q1JCR Q1Corr. AuthorIF: 8.2
27. Nawaz, M. S., Fournier-Viger, P., Nawaz, S., Zhu, H., Yun U. (2024). SPM4GAC: SPM based approach for genome analysis and classification of macromolecules. International Journal of Biological Macromolecules (BIOMAC). Elsevier, Volume 266, Part 2, May 2024, article 130984
DOI: 10.1016/j.ijbiomac.2024.130984		 EISCICAS Q1JCR Q1Corr. AuthorIF: 8.2
28. Lee, C., Kim, H., Cho, M., Kim, H., Vo, B., Chun-Wei Lin, J., Fournier-Viger, P., Yun, U. (2024) Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database. IEEE Access 12: 77605-77620 EISCI
29. Nawaz, M. S., Nawaz, M. Z., Gong, Y., Fournier-Viger, P., Diallo, A. B. (2024). In-Silico Framework for Genome Analysis. Future Generation Computer Systems, Elsevier, Volume 164, March 2025, article 107585.
DOI: 10.1016/j.future.2024.107585		 EISCICAS Q2CCF-C Corr. Author
30. Nawaz, S. M., Nawaz, Z., Zhang, J., Fournier-Viger, P., Qu, J. (2024). Exploiting the Sequential Nature of Genomic Data for Improved Analysis and Identification. Computers in Biology and Medicine (CIBM), Elsevier, 183:109307
DOI: 10.1016/j.compbiomed.2024.109307		 EISCICAS Q2JCR Q1Corr. AuthorIF: 6.6
31. Trasierras, A. M., Luna, J. M., Fournier-Viger, P., Ventura, S. (2024). Data heterogeneity's impact on the performance of frequent itemset mining algorithms. Information Science, 678: 120981. EISCICAS Q2JCR Q1CCF-BIF: 5.3
32. Chen, Z., He, C., Chen, G., Gan, W., Fournier-Viger P. (2024). HUSM: High utility subgraph mining in single graph databases. Information Science, 675: 120743 EISCICAS Q2JCR Q1CCF-BIF: 5.3
33. Yan, Y., Niu, X., Zhang, Z., Fournier-Viger, P., Ye, L., Min, F. (2024). Efficient high utility itemset mining without the join operation.. Information science, Volume 665, Issue C.
DOI: 10.1016/j.ins.2024.120392		 EISCICAS Q2JCR Q1CCF-BIF: 5.3
34. Song, W., Sun, Z., Fournier-Viger, P., Wu, Y. (2024) MRI-CE: Minimal rare itemset discovery using the cross-entropy method. Information science, Volume 665, Issue C.
DOI: 10.1016/j.ins.2024.120392		 EISCICAS Q2JCR Q1CCF-BIF: 5.3
35. Duong, H.V., Truong, T.C., Le, B., Fournier-Viger, P. (2024). CG-FHAUI: an efficient algorithm for simultaneously mining succinct pattern sets of frequent high average utility itemsets. Knowledge and Information Systems (KAIS), 66(9): 5239-5280 EISCICAS Q4JCR Q3CCF-BIF: 2.39
36. Baek, Y., Kim, H., Cho, M., Kim, H., Lee, C., Ryu, T., Kim, H., Vo, B., Gan, V., Fournier-Viger, P., Lin, J C.-W., Pedrycz, W., Yun, U. (2024). An Efficient Approach for Incremental Erasable Utility Pattern Mining from non-binary Data. Knowledge and Information Systems (KAIS), 66: 5919–5958.
DOI: 10.1007/s10115-024-02185-5		 EISCICAS Q4JCR Q3CCF-BIF: 2.39
37. Duong, H., V., Truong, T. C., Le, B., Fournier-Viger P. (2024). Mining Interesting Sequential Patterns using a Novel Balanced Utility Measure. Knowledge-Based Systems (KBS), 294: 111796 (2024)
DOI: 10.1016/j.knosys.2024.111796		 EISCI CAS Q2JCR Q1CCF-CIF: 5.3
38. Zhu, J., Niu, X., Li, F., Wang, Y., Fournier-Viger, P., She, K. (2024). STTraj2Vec: A spatio-temporal trajectory representation learning approach. Knowledge-Based Systems (KBS), Elsevier, Volume 300, article 112207
DOI: 10.1016/j.knosys.2024.112207		 EISCICAS Q2JCR Q1CCF-CIF: 5.3
39. Chen X., Liu, T., Fournier-Viger, P., Zhang, B., Long, G., Zhang, Q. (2024). A Fine-grained Self-adapting Prompt Learning Approach for Few-shot Learning with Pre-trained Language Models. Knowledge-Based Systems (KBS), Elsevier, Volume 299, article 111968.
DOI: 10.1016/j.knosys.2024.111968		 EISCICAS Q2JCR Q1CCF-CIF: 5.3
40. He, Y.-L., Yu, J.-Y., Li, X., Fournier-Viger, P., Huang, J. Z.(2024). A novel and efficient risk minimisation-based missing value imputation algorithm. Knowledge-Based Systems (KBS), Elsevier, 304: 112435
DOI: 10.1016/j.knosys.2024.112435		 EISCICAS Q2JCR Q1CCF-CIF: 5.3
41. Duong, H., Quang, H., Truong, T., Fournier-Viger, P. (2024). Efficient Algorithms to Mine Concise Representations of Frequent High Utility Occupancy Patterns. Applied Intelligence, 54(5): 4012-4042,
DOI: 10.1007/s10489-024-05296-2
 EISCICAS Q3JCR Q2CCF-CCorr. Author
42. He, Y., Fournier-Viger, P., Ventura, S., Zhang, L. (2024). Introduction to the Special Issue on Recent Advances on Digital Economy-Oriented Artificial Intelligence. Engineering Applications of Artificial Intelligence (EAAI), to appear (editorial for special issue), Volume 137, Part A, November 2024, 109087.
DOI: 10.1016/j.engappai.2024.109087		 EISCICAS Q2JCR Q1CCF-CIF: 2.8
43. He, Y., Wu, D.-T., Fournier-Viger, P., H., Z..  First Filling Strategy-Based Partitioning Method to Balance Data in Spark. Acta Electronica Sinica, 2024, 52(10): 3322-3335. DOI: 10.12263/DZXB.20240094
44. He, Y., Lu, X., Fournier-Viger, P., Zhexue Huang, J. (2024). A novel overlapping minimization SMOTE algorithm for imbalanced classification. Frontiers Inf. Technol. Electron. Eng. 25(9): 1266-1281 (2024)
DOI: 10.1631/FITEE.2300278		 EISCI
45. Govan, R., Scherrer, R. Fougeron, B., Laporte-Magoni, C., Thibeaux, R., Genthon, P., Fournier-Viger, P., Goarant, C., Selmaoui-Folcher, N. (2024). Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach. PLOS Neglected Tropical Diseases. (to appear)
DOI: 10.1371/journal.pntd.0012755
46. Frnda, J., Durica, M., Lin, J. C.-W., Fournier-Viger, P. (2024) Video dataset containing video quality assessment scores obtained from standardized objective and subjective testing. Data in Brief. Volume 54, June 2024, 110458
DOI: 10.1016/j.dib.2024.110458
2023. 47. Geng, M. Wu, Y., Li, Y., Liu, J., Fournier-Viger, P. Zhu, X., Wu, X. (2023). RNP-Miner: repetitive non overlapping sequential pattern mining. IEEE Transactions on Knowledge and Data Engineering (TKDE), 36(9):4874-4889.
DOI: 10.1109/TKDE.2023.3334300		 EISCICAS Q2JCR Q1CCF-AIF: 4.561
48. Wu, Y., Meng, Y., Li, Y., Guo, L., Zhu, X., Fournier-Viger, P. (2023). COPP-Miner: Top-k contrast order-preserving pattern mining for time series classification. IEEE Transactions on Knowledge and Data Engineering (TKDE), 36(6):2372-2387.
DOI: 10.1109/TKDE.2023.3321749		 EISCICAS Q2JCR Q1CCF-AIF: 4.561
49. Qu, J.-F., Fournier-Viger, P., Liu, M., Hang, B., Hu, C. (2023). Mining High Utility Itemsets Using Prefix Trees and Utility Vectors. IEEE Transactions on Knowledge and Data Engineering (TKDE), 35(10):10224-10236
DOI: 10.1109/TKDE.2023.3256126		 EISCICAS Q2JCR Q1CCF-AIF: 4.561
50. Wu. Y., Zhao, X., Li, Y., Guo, L., Zhu, X., Fournier-Viger, P., Wu, X. (2023). OPR-Miner: Order-preserving rule mining for time series. IEEE Transactions on Knowledge and Data Engineering (TKDE)， 35(11):11722-11735
DOI: 10.1109/TKDE.2022.3224963		 EISCICAS Q2JCR Q1CCF-AIF: 4.561
51. Ouarem, O., Nouioua, F., Fournier-Viger, P. (2023). A Survey of Episode Mining. WIREs Data Mining and Knowledge Discovery, Wiley,14(2):e1524.
DOI: 10.1002/widm.1524		 EISCICAS Q3JCR Q1Corr. AuthorIF： 7.8
52. Luna, J. M., Kiran, R. U., Fournier-Viger, P., Vventura, S. (2023).Efficient Mining of Top-k High Utility Itemsets through Genetic Algorithms. Information Sciences, Elsevier, 624: 529-553.
DOI: 10.1016/j.ins.2022.12.092		 EISCICAS Q2JCR Q1CCF-BIF: 5.3
53. Gan, W., Huang, S., Miao, J., Han, X., Fournier-Viger, P. (2023).Targeted Mining of Top-k High Utility ltemsets. Engineering Applications of Artificial Intelligence (EAAI), Elsevier, Volume 126, Part D, November 2023, 107047
DOI: 10.1016/j.engappai.2023.107047		 EISCICAS Q2JCR Q1CCF-C
54. Nawaz, S. M., Fournier-Viger, P., He, Y., Zhang, Q. (2023). PSAC-PDB: Analysis and Classification of Protein Structures. Computers in Biology and Medicine (CIBM), Elsevier, 158: 106814 (2023)
DOI: 10.1016/j.compbiomed.2023.106814		 EISCICAS Q2JCR Q1Corr. AuthorIF: 6.6
55. Ouarem, O., Nouioua, F., Fournier-Viger. (2023). Discovering Frequent Parallel Episodes in Complex Event Sequences by Counting Distinct Occurrences. Applied Intelligence, 54(11-12): 701-721
DOI: 10.1007/s10489-023-05187-y
 EISCICAS Q3JCR Q2CCF-CCorr. Author IF: 3.264
56. Nawaz, S., Fournier-Viger, P., Aslam, M., Li, W., He, Y., Niu, X. .(2023). Using Alignment-Free and Pattern Mining Methods for SARS-CoV-2 Genome Analysis. Applied Intelligence, 53:21920–21943.
DOI: 10.1007/s10489-023-04618-0
 EISCICAS Q3JCR Q2CCF-CCorr. Author IF: 3.264
57. Ahmed, U., Lin, J. C.-W., Fournier-Viger, P. (2023). Federated deep active learning for attention-based transaction classification. Applied Intelligence, 53(8): 8631-8643
DOI : 10.1007/s10489-022-04388-1		 EISCICAS Q3JCR Q2CCF-CIF: 3.264
58. He, Y., Li, X., Zhang, M., Fournier-Viger, P., Huang, J. Z. (2023). A Novel Observation Points-Based Positive-Unlabeled Learning Algorithm. CAAI Transactions on Intelligence Technology, to appear.
DOI: 10.1049/cit2.12100.
 EISCI
59. Aslam, M., Nawaz, M. S., Fournier-Viger, P., Li, W. (2023). Comparative Analysis and Classification of SARS-CoV-2 Spike Protein Structures in PDB. COVID journal, 3(4):452-471
DOI: 10.3390/covid3040034
60. Wang, Z., Fournier-Viger, P., Wang, Y.-P., Fu, Y., (2023). Crosstalk between Computational Medicine and Neuroscience in Healthcare. Frontiers in neurosciences (editorial article)
2022. 61. He, Y., Ye, X, Cui, L, Fournier-Viger, P., Luo, C., Huang, J. Z., Suganthan, P. N. (2022) Wireless Network Slice Assignment with Incremental Random Vector Functional Link Network. IEEE Transactions on Network Science and Engineering, 10(3): 1283-1296
DOI: 10.1109/TNSE.2022.3178740		 SCI
62. He, Y., Ye, X., Huang D., Fournier-Viger, P., Huang, J. Z. (2022). A Hybrid Method to Measure Distribution Consistency of Mixed-Attribute Data Sets. IEEE Transactions on Artificial Intelligence, IEEE, 4(1): 182-196.
DOI:  10.1109/TAI.2022.3151724
63. Ye, X., He, Y., Zhang, M., Fournier-Viger, P., Huang, J. Z. (2022). A Novel Correlation Gaussian Process Regression-Based Extreme Learning Machine. Knowledge and Information Systems (KAIS), Springer, 65(5): 2017-2042
DOI: 10.1007/s10115-022-01803-4		 EISCICAS Q3JCR Q2CCF-B
64. He, Y., Ye, X, Huang, J. Z., Fournier-Viger, P. (2022). Bayesian Attribute Bagging-Based Extreme Learning Machine for High-dimensional Classification and Regression. ACM Trans. Intell. Syst. Technol., ACM, to appear.
DOI: 10.1145/3495164
 EISCICAS Q3JCR Q1IF: 4.6
65. Gan, W., Chen, G., Yin, H., Fournier-Viger, P., Chen, C., Yu, P. S. (2022). Towards Revenue Maximization with Popular and Profitable Products . ACM Transactions on Data Science), 2(4): article 42.
DOI: 10.1145/3488058
66. Nguyen, T.-T., Nguyen, L. T.T., Nguyen, T. D. D., Fournier-Viger, P., Nguyen, N.-T., Vo, B. (2022). Efficient mining of cross-level high-utility itemsets in taxonomy quantitative databases. Information Sciences, Elsevier, to appear.
DOI: 10.1016/j.ins.2021.12.017		 EISCICAS Q2JCR Q1CCF-BIF: 5.3
67. Le, B., Truong, T., Duong, H., Fournier-Viger, P., Fujita, H. (2022) H-FHAUI: Hiding Frequent High Average Utility Itemsets. Information Sciences, Elsevier, to appear.
DOI: 10.1016/j.ins.2022.07.027		 EISCICAS Q2JCR Q1CCF-BIF: 5.3
68. Liu, J., Fournier-Viger, P., Zhou, M., He, G., Nouioua, M. (2022). CSPM: Discovering Compressing Stars in Attributed Graphs. Information Sciences, Elsevier, 609: 172-194
DOI: 10.1016/j.ins.2022.08.008		 EISCICAS Q2JCR Q1CCF-BCorr. AuthorIF: 5.3
69. Zhang, X., Qi, Y., Chen, G., Gan, W., Fournier-Viger, P.... (2022). Fuzzy-driven Periodic Frequent Pattern Mining. Information Sciences, Elsevier, to appear.
DOI: 10.1016/j.ins.2022.11.009		 EISCICAS Q2JCR Q1CCF-BIF: 5.3
70. Wang, S., Niu, X., Fournier-Viger, P., Zhou, D., Min, F. (2022) A Graph Based Approach for Mining Significant Places in Trajectory Data. Information Sciences, Elsevier
DOI: 10.1016/j.ins.2022.07.046		 EISCICAS Q2JCR Q1CCF-BIF: 5.3
71. Wu, Y., Yuan, Z., Li, Y., Guo, L, Fournier-Viger, P., Wu, X. (2022). Nonoverlapping Weak-gap Sequential Pattern Mining. Information Sciences, Elsevier, to appear.
DOI: 10.1016/j.ins.2021.12.064		 EISCICAS Q2JCR Q1CCF-BIF: 5.3
72. He, Y., Li, X., Huang, J. Z., Fournier-Viger, P., Li, M. (2022). Observation Points Classifier Ensemble for High-Dimensional Imbalanced Classification. CAAI Transactions on Intelligence Technology, to appear.
DOI: 10.1049/cit2.12100
SCI
73. Xu, G., Xian, D., Fournier-Viger, P., Li, X., Ye, Y., Hu, X. (2022). AM-ConvGRU: A Spatio-Temporal Model for Typhoon Path Prediction. Neural Computing and Applications, Springer, to appear.
DOI: 10.1007/s00521-021-06724-x		 EISCICAS Q2JCR Q1CCF-CIF: 5.6
74. Song, W., Ye, W., Fournier-Viger, P. (2022). Mining sequential patterns with flexible constraints from MOOC data. Applied Intelligence, to appear.
DOI : 10.1007/s10489-021-03122-7		 EISCICAS Q3JCR Q2CCF-CIF: 3.264
75. Amirat, H., Lagraa, N., Fournier-Viger, P., Ouinten, Y., Kherfi, M. L., Guellouma, Y. (2022) Incremental Tree-based Successive POI Recommendation in Location-based Social Networks . Applied Intelligence, 53(7): 7562-7598
DOI:10.1007/s10489-022-03842-4		 EISCICAS Q3JCR Q2CCF-CIF: 3.264
76. Nouioua, F., Lekfir, M., Fournier-Viger, P. (2022). Hiding Sensitive Frequent Itemsets by Item Removal via Two-Level Multi-Objective Optimization. Applied Intelligence, Springer, to appear.
DOI: 10.1007/s10489-022-03808-6		 EISCICAS Q3JCR Q2CCF-CCorr. AuthorIF: 3.264
77. He, Y., . Ou,  G.-L., Philippe Fournier-Viger, Huang, J. Z., Suganthan, P. N.,  (2022). A novel dependency-oriented mixed-attribute data classification method. Expert Systems with Applications (ESWA), Elsevier, to appear.
DOI: 10.1016/j.eswa.2022.116782		 EISCICCF-C
78. Nawaz, M. S., Fournier-Viger, P., Nawaz, M. Z., Chen, G., Wu, Y. (2022) MalSPM: Metamorphic Malware Behavior Analysis and Classification using Sequential Pattern Mining. Computers & Security, Elsever, to appear
DOI: 10.1016/j.cose.2022.102741		 EISCICAS Q3CCF-BCorr. AuthorIF: 4.438
79. Yun, U. ... Fournier-Viger, P. ... (2022) Mining High Occupancy Patterns to Analyze Incremental Dynamic Data in Intelligent Systems. ISA Transactions, Elsevier, to appear.
DOI: 10.1016/j.isatra.2022.05.003		 EISCICAS Q2IF: 5.468
80. Duong, H., Hoang, T., Tran, T., Truong, T., Le, B., Fournier-Viger, P. (2022). Efficient Algorithms for Mining Closed and Maximal High Utility Itemsets. Knowledge-Based Systems (KBS), Elsevier, to appear. EISCICAS Q2JCR Q1CCF-CIF: 5.3
81. Wu., P., Niu, X., Fournier-Viger, P., Huang, C., Wang, B. (2022). UBP-Miner: An Efficient Bit Based High Utility Itemset Mining Algorithm. Knowledge-Based Systems (KBS), Elsevier, to appear.
DOI : 10.1016/j.knosys.2022.108865		 EISCICAS Q2JCR Q1CCF-CIF: 5.3
2021. 82. Gan, W., Lin, J. C. W., Zhang, J., Yin, H., Fournier-Viger, P., Chao, H.-C., Yu, P. S. (2021). Utility Mining Across Multi-Dimensional Sequences. ACM Transactions on Knowledge Discovery from Data (TKDD), 15(5): 82:1-82:24. EISCICAS Q4JCR Q2CCF-BIF: 2.8
83. Wu, Y., Luo, L., Li, Y., Guo, L., Fournier-Viger, P., Wu, X. (2019). NTP-Miner: Nonoverlapping three-way sequential pattern mining. ACM Transactions on Knowledge Discovery from Data (TKDD), to appear. EISCICAS Q4JCR Q2CCF-BIF: 2.8
84. Wu, Y., Geng, M., Li, Y., Guo, L, Li, Z., Fournier-Viger, P., Zhu, X., Wu, X. (2021). HANP-Miner: High Average Utility Nonoverlapping Sequential Pattern Mining. Knowledge-Based Systems (KBS), Elsevier, to appear. EISCICAS Q2JCR Q1CCF-CIF: 5.3
85. Wang, Y., Wu, Y., Li, Y., Yao, F., Fournier-Viger, P., Wu, X. (2021). Self-Adaptive Nonoverlapping Sequential Pattern Mining. Applied Intelligence, to appear. EISCICAS Q3JCR Q2CCF-CIF: 3.264
86. Fournier-Viger, P., Wang Y., Yang, P., Lin, J. C.-W., Yun, U. (2021). TSPIN: Mining Top-k Stable Periodic Patterns. Applied Intelligence, to appear. [source code & data] EISCICAS Q3JCR Q2CCF-CCorr. AuthorIF: 3.264
87. Nouioua, M., Fournier-Viger, P., W. C.-W., Lin, J. C.-W., Gan, W. (2021). FHUQI-Miner: Fast High Utility Quantitative Itemset Mining. Applied Intelligence, to appear. [source code & data] [ppt] EISCICAS Q3JCR Q2CCF-CCorr. AuthorIF: 3.264
88. Chi, T. T., Duong, H., Le, B, Fournier-Viger, P., Yun, U. (2021). Frequent High Minimum Average Utility Sequence Mining with Constraints in Dynamic Databases using Efficient Pruning Strategies. Applied Intelligence, to appear. EISCICAS Q3JCR Q2CCF-CCorr. AuthorIF: 3.264
89. Chi, T. T., Duong, H., Le, B, Fournier-Viger, P., Yun, U. (2021). Mining Interesting Sequences with Low Average Cost and High Average Utility. Applied Intelligence, to appear. EISCICAS Q3JCR Q2CCF-CCorr. AuthorIF: 3.264
90. Nawaz, S., Fournier-Viger, P., Shojaee, A., Fujita, H. (2021). Using Artificial Intelligence Techniques for COVID-19 Genome Analysis. Applied Intelligence, 51(5):3086-3103 [source code and data + SPMF] EISCICAS Q3JCR Q2CCF-CCorr. AuthorIF: 3.264
91. Huynh, H., Nguyen, L., Vo, B., Oplatková, Z., Fournier-Viger, P., Yun, U. (2021). An Efficient Parallel Algorithm for Mining Weighted Clickstream Patterns. Information Sciences, Elsevier, 553: 353-375.
DOI:
 EISCICAS Q2JCR Q1CCF-BIF: 5.3
92. Nawaz, M.S., Nawaz, M.Z., Hasan, O, Fournier-Viger, P., Sun, M. (2021). An Evolutionary/Heuristic-Based Proof Searching Framework for Interactive Theorem Prover. Applied Soft Computing, Elsevier. 104: 107200
DOI: 10.1016/j.asoc.2021.107200		 EISCICAS Q1JCR Q1Corr. AuthorIF: 5.472
93. Nawaz, M.S., Fournier-Viger, P., Yun, U., Wu, Y., Song, W. (2021). Mining High Utility Itemsets with Hill Climbing and Simulated Annealing. ACM Transactions on Management Information Systems (to appear) EISCICAS Q3JCR Q1Corr. AuthorIF: 4.6
94. Kim, H., Yun, U., Baek, Y., Kim, H., Nam, H., Lin, J. C.-W., Fournier-Viger, P. (2021). Damped Sliding Window based Utility Pattern Mining over Stream Data. Knowledge-based Systems, Elsevier, 213(15):106653
DOI: 10.1016/j.knosys.2020.106653		 EISCICAS Q2JCR Q1CCF-CIF: 5.3
95. Fujita, H., Fournier-Viger, P., Sasaki, J., Ali, M. (2021). Advances in Theory and Applications of Artificial Intelligence. AI Magazine, 42 (1):86-87, AAAI. (conference report) EISCICAS Q4JCR Q2IF: 2.1
96. Bouakkaz, M., Ouinten, Y., Loudcher, S., Fournier-Viger, P. (2021), Enhancing link prediction in dynamic networks using content aggregation. Cluster Computing, DOI: 10.1007/s10586-021-03290-8 EISCICAS Q3JCR Q3IF: 3.47
97. Lin, J. C.-W., Djenouri, Y., Srivastava, G., Fournier-Viger, P. (2021). Mining Profitable and Concise Patterns in Large-Scale Internet of Things Environments. Wireless Communications and Mobile Computing, Hindawi, DOI: 10.1155/2021/6653816
98. Wu, J. M.-T., Srivastava, G., Jolfaei, A., Fournier-Viger, P., Lin, J.C.W.(2021). Hiding Sensitive Information in eHealth Datasets. Future Generation Computer Systems, Elsevier, 117: 169-180. EISCICAS Q2CCF-CIF: 5.768
2020. 99. Gan, W., Lin, J. C.-W., Fournier-Viger, P., Zhang. J., Chao, H.-C., Yu, P. S. (2020). Fast Utility Mining on Sequence Data. IEEE Transactions on Cybernetics. IEEE. 51(2), 487-500
DOI: 10.1109/TCYB.2020.2970176		 EISCICAS Q1JCR Q1CCF-BIF: 11.469
100. Qu, J., Fournier-Viger, P., Liu, M., Hang, B., Wang, F. (2020) Mining High Utility Itemsets Using Extended Chain Structure and Utility Machine. Knowledge-Based Systems (KBS), Elsevier, 208:106457
DOI: 10.1016/j.knosys.2020.106457		 EISCICAS Q2JCR Q1CCF-CIF: 5.3
101. Gan, W., Lin, J., Fournier-Viger, P., Chao, H.C., Yu, P.S. (2020). Beyond Frequency: Utility Mining with Varied Item-Specific Minimum Utility. ACM Transactions on Internet Technology, ACM, 21(1): 3:1-3:32.
DOI: 10.1145/3425498		 EISCICAS Q3JCR Q2CCF-BIF: 3.846
102. Shabtay, L., Fournier-Viger, P., Yaari, R., Dattner, I. (2020). A Guided FP-growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data. Information Sciences, Elsevier, 553: 353-375.
DOI: 10.1016/j.ins.2020.10.020
 EISCICAS Q2JCR Q1CCF-BIF: 5.3
103. Fournier-Viger, P., Yang, P., Kiran, R. U., Ventura, S., Luna, J. M.. (2020). Mining Local Periodic Patterns in a Discrete Sequence. Information Sciences, Elsevier, 544: 519-548. [source code] [ppt]
DOI: 10.1016/j.ins.2020.09.044		 EISCICAS Q2JCR Q1CCF-BCorr. AuthorIF: 5.3
104. Fournier-Viger, P., He, G., Cheng, C., Li, J., Zhou, M., Lin, J.C-W., Yun, U. (2020). A Survey of Pattern Mining in Dynamic Graphs. WIREs Data Mining and Knowledge Discovery, Wiley, 10(6).
DOI: 10.1002/WIDM.1372		 EISCICorr. Author
105. Liu, X., Niu, X., Fournier-Viger, P. (2020) Fast Top-K Association Rule Mining Using Rule Generation Property Pruning. Applied Intelligence, Springer, to appear
DOI: 10.1007/s10489-020-01994-9
 EISCICAS Q3JCR Q2CCF-CCorr. AuthorIF: 3.264
106. Nawaz, S., Nawaz, Z, Hasan, O., Fournier-Viger, P., Sun, M. (2020) Proof Searching and Prediction in HOL4 with Evolutionary/Heuristic and Deep Learning Techniques. Applied Intelligence, Springer, to appear
DOI: 10.1007/s10489-020-01837-7		 EISCICAS Q3JCR Q2CCF-CCorr. AuthorIF: 3.264
107. Noor, S., Guo, Y., Shah, S. H. H., Fournier-Viger, P., Nawaz, M. S. (2020). Analysis of Public Reaction to the Novel Coronavirus (COVID-19) Outbreak on Twitter. Kybernetes, Emerald Publishing
DOI: 10.1108/K-05-2020-0258		 SCIIF: 2.39
108. Lin, J., Li, Y., Fournier-Viger, P., Djenouri, Y., Zhang, J. (2020). Efficient Chain Structure for High-Utility Sequential Pattern Mining. IEEE Access, 8：40714-40722
DOI: 10.1109/ACCESS.2020.2976662		 EISCICAS Q2JCR Q1IF: 4.098
109. Nawaz, S., Fournier-Viger, P., Lin, J. C.-W., Zhang, J. (2020). Proof Learning in PVS with Utility Pattern Mining. IEEE Access, 8: 119806-119818
DOI: 10.1109/ACCESS.2020.3004199		 EISCICAS Q2JCR Q1Corr. AuthorIF: 4.098
110. Vo, B., Nguyen, T.T., Nguyen, T. D. D., Fournier-Viger, P., Yun, U. (2020). A multi-core approach to efficiently mine high-utility itemsets in dynamic profit databases. IEEE Access, 9(1):85890-85899.
DOI: 10.1109/ACCESS.2020.2992729		 EISCICAS Q2JCR Q1IF: 4.098
111. Luna, J.M., Fournier-Viger, P., Ventura, S. (2020). Extracting user-centric knowledge on two different spaces: concepts and records. IEEE Access, 8(1):134782-134799.
DOI: 10.1109/ACCESS.2020.3010852		 EISCICAS Q2JCR Q1IF: 4.098
112. Li, X., Li, J., Fournier-Viger, P., Nawaz, M. S., Yao, J., Lin, J. C.-W. Mining Productive Itemsets in Dynamic Databases. IEEE Access, 8:140122-140144.
DOI: 10.1109/ACCESS.2020.3012817		 EISCICAS Q2JCR Q1Corr. AuthorIF: 4.098
113. Frnda, J., Durica, M., Savrasovs, M., Fournier-Viger, P., Lin, J. C.-W. (2020). QoS to QoE Mapping Function for Iptv Quality Assessement Based on Kohonen Map: A Pilot Study. Transport and Telecommunication, Sciendo, 21(3), 181-190.
DOI: 10.2478/ttj-2020-0014		 SCI
114. Frnda, J., ... Fournier-Viger, P. ... (2020). A New Perceptual Evaluation Method of Video Quality Based on Neural Network. Intelligent Data Analysis, IOS Press, 25(3): 571-587.
DOI: 10.3233/IDA-205085		 EISCICAS Q4JCR Q4CCF-CIF: 0.86
115. Yun, U., Kim, J., Yoon, E., Lin, J.C.W., Fournier-Viger, P. (2020). One scan based High Average-Utility Pattern Mining in static and dynamic databases. Future Generation Computer Systems, Elsevier, to appear.
DOI: 10.1016/j.future.2020.04.027		 EISCICAS Q2CCF-CIF: 5.768
116. Truong, T.T., Tran, A., Duong, H., Le, B., Fournier-Viger, P. (2020). EHUSM: Mining High Utility Sequences with a Pessimistic Utility Model. Data Science and Pattern Recognition (DSPR), to appear.
117. Fournier-Viger, P., Li, J., Lin, J. C.-W., Truong, T. (2020). Discovering low-cost high utility patterns. Data Science and Pattern Recognition (DSPR), Vol. 4(2), pp. 50–64, 2020. Corr. Author
2019 118. Baek, Y., Yun, U., Yoon, E., Fournier-Viger P. d(2019). Uncertainty based Pattern Mining for Maximizing Profit of Manufacturing Plants with List Structure. IEEE Transactions on Industrial Electronics, IEEE. 67(11):9914-9926.
DOI: 10.1109/TIE.2019.2956387		 EISCICAS Q1JCR Q1IF: 7.05
119. Gan, W., Lin, J. C.-W., Fournier-Viger, P., Chao, H.-C., Tseng, V. S., Yu, P. (2019). A Survey of Utility-Oriented Pattern Mining. IEEE Transactions on Knowledge and Data Engineering (TKDE), to appear...
DOI: 10.1109/TKDE.2019.2942594		 EISCICAS Q2JCR Q1CCF-AIF: 4.561
120. Truong, T., Duong, H., Le, B., Fournier-Viger, P. (2019). Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds. IEEE Transactions on Knowledge and Data Engineering (TKDE), 31(2): 301 - 314. EISCICAS Q2JCR Q1CCF-AIF: 4.561
121. Fournier-Viger, P., Li, J., Lin, J. C., Chi, T. T., Kiran, R. U. (2019). Mining Cost-Effective Patterns in Event Logs. Knowledge-Based Systems (KBS), Elsevier, 191: 105241 [ppt] [source code]
DOI: 10.1016/j.knosys.2019.105241		 EISCICAS Q2JCR Q1CCF-CCorr. AuthorIF: 5.3
122. Fournier-Viger, P., Cheng, C., Cheng, Z., Lin, J. C.-W., Selmaoui-Folcher, N. (2019). Mining Significant Trend Sequences in Dynamic Attributed Graphs. Knowledge-Based Systems (KBS), Elsevier, to appear. [source code] [ppt]
DOI: 10.1016/j.knosys.2019.06.005		 EISCICAS Q2JCR Q1CCF-CCorr. AuthorIF: 5.3
123. Truong, T., Duong, H., Le, B., Fournier-Viger, P., Yun, U. (2019). Efficient High Average-Utility Itemset Mining Using Novel Vertical Weak Upper-Bounds. Knowledge-Based Systems (KBS), Elsevier, 31(2): 301-314
DOI: 10.1016/j.knosys.2019.07.018		 EISCICAS Q2JCR Q1CCF-CCorr. AuthorIF: 5.3
124. Nguyen, L, Nguyen, P., Nguyen, T., Vo, B., Fournier-Viger, P., Tseng, V. S. (2019). Mining High Utility Itemsets in Dynamic Profit Databases. Knowledge-Based Systems (KBS), Elsevier,175: 130-144.
DOI: 10.1016/j.knosys.2019.03.022		 EISCICAS Q2JCR Q1CCF-CIF: 5.3
125. Fournier-Viger, P., Zhang, Y., Lin, J. C.W., Fujita, H., Koh, Y.S. (2019). Mining Local and Peak High Utility Itemsets. Information Sciences, Elsevier, 481: 344-367. [source code][ppt] DOI: 10.1016/j.ins.2018.12.070 EISCICAS Q2JCR Q1CCF-BCorr. AuthorIF: 5.3
126. Fournier-Viger, P., Li, Z., Lin, J. C. W., Kiran, R. U., Fujita, H. (2019). Efficient Algorithms to Identify Periodic Patterns in Multiple Sequences. Information Sciences, Elsevier, 489:205-226 [source code][ppt]
DOI: 10.1016/j.ins.2019.03.050		 EISCICAS Q2JCR Q1CCF-BCorr. AuthorIF: 5.3
127. Djenouri, Y., Djenouri, D., Belhadi, A., Fournier-Viger, P., Lin, J. C.-W., Bendjoudi, A. (2019). Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases. Information Sciences, Elsevier, 496: 326-342 (2019) EISCICAS Q2JCR Q1CCF-BIF: 5.3
128. Truong, T., Duong, H., Le, B., Fournier-Viger, P. (2019). EHAUSM: An Efficient Algorithm for High Average Utility Sequence Mining. Information Sciences, Elsevier, 515:302-323.
DOI: 10.1016/j.ins.2019.11.018		 EISCICAS Q2JCR Q1CCF-BIF: 5.3
129. Lin, J. C.-W., Shao, Y., Fournier-Viger, P., Fujita, H. (2019). BILU-NEMH: A BILU Neural-Encoded Mention Hypergraph for Mention-Extraction. Information Sciences, Elsevier, 496: 53-64.
DOI: 10.1016/j.ins.2019.04.059		 EISCICAS Q2JCR Q1CCF-BIF: 5.3
130. Fournier-Viger, P., Yang, P., Li, Z., Lin, J. C.-W., Kiran. R. U. (2019). Discovering Rare Correlated Periodic Patterns in Multiple Sequences. Data & Knowledge Engineering (DKE), 126: DOI: 101733, 10.1016/j.datak.2019.101733.
 EISCICAS Q4JCR Q3CCF-BCorr. Author IF: 1.46
131. Fournier-Viger, P., Yang, P., Lin, J. C.-W, Duong, Q.-H., Dam, T.-L., Sevcik, L., Uhrin, D., Voznak, M. (2019). Discovering Periodic Itemsets using Novel Periodicity Measures. Advances in Electrical and Electronic Engineering, 17(1):33-44, DOI: 10.15598/aeee.v17i1.3185. Corr. Author
132. Luna, J. M., Fournier-Viger, P., Ventura, S. (2019). Frequent Itemset Mining: a 25 Years Review. WIREs Data Mining and Knowledge Discovery, Wiley, 9(6):e1329.
DOI: 10.1002/widm.1329		 EISCICAS Q3JCR Q2IF: 4.703
133. Gan, W., Lin, J. C.-W., Fournier-Viger, P., Chao, H.-C., Yu, P. S. (2019). HUOPM: High Utility Occupancy Pattern Mining. IEEE Transactions on Cybernetics. IEEE. 50(3): 1195-1208.
DOI: 10.1109/TCYB.2019.2896267		 EISCICAS Q1JCR Q1CCF-BIF: 11.469Web-of-Science Highly-Cited paper
134. Gan, W., Lin, J. C. W., Fournier-Viger, P., Chao, H.-C., Yu, P. S. (2019) A Survey of Parallel Sequential Pattern Mining. ACM Transactions on Knowledge Discovery from Data (TKDD), 13(3): 25:1-25:34.
DOI: 10.1145/3314107		 EISCICAS Q4JCR Q2CCF-BIF: 2.8
135. Chi, T. T., Duong, H., Le, B., Fournier-Viger, P. (2019). FMaxCloHUSM: An Efficient Algorithm for Mining Frequent Closed and Maximal High Utility Sequences. Engineering Applications of Artificial Intelligence (EAAI), Elsevier, 85: 1-20.
DOI: 10.1016/j.engappai.2019.05.010		 EISCICAS Q2JCR Q1CCF-CIF: 2.8
136. Lin, J. C.-W., Wu, J. M-T., Fournier-Viger, P., Chen, C.-H., Zhang, Y. (2019). A Sanitization Approach to Secure Shared Data in an IoT Environment. IEEE Access, 7:25359-25368.
DOI: 10.1109/access.2019.2899831		 EISCICAS Q2JCR Q1IF: 4.098
137. Lin, J. C.-W., Zhang, S., Zhang, Y., Zhang, B., Fournier-Viger, P., Djenouri, Y. (2019). Hiding Sensitive Itemsets with Multiple Objective Optimization. Soft computing, Springer, 23 (23): 12779–12797.
DOI: 10.1007/s0050		 EISCICAS Q3JCR Q2CCF-CIF: 2.367
138. Amirat, H., Lagraa, N., Fournier-Viger, P., Ouinten, Y. (2019). NextRoute: A lossless model for accurate mobility prediction. Journal of Ambient Intelligence and Humanized Computing, Springer. (to appear)
DOI: 10.1007/s12652-019-01327-w		 EISCICAS Q4JCR Q3IF: 1.910
139. Win, K. N., Chen, J., Chen, Y., Fournier-Viger, P. (2019) PCPD: A Parallel Crime Pattern Discovery System for Large-scale Spatio-temporal Data based on Fuzzy Clustering. International Journal of Fuzzy Systems, Springer, 21(6):1961–1974.
DOI: 10.1007/s40815-019-00673-3		 EISCICAS Q3JCR Q2IF: 2.39
140. Lin, J. C.-W., Li, T., Pirouz, M., Zhang, J., Fournier-Viger, P. (2019). High Average-Utility Sequential Pattern Mining based on Uncertain Databases. Knowledge and Information Systems (KAIS), Springer, 56(1): 165-196.
DOI: 10.1007/s10115-019-01385-8		 EISCICAS Q3JCR Q2CCF-BIF: 2.39
141. Win, K. N., Li, K., Chen, J., Fournier-Viger, P., Li, K. (2019). Fingerprint Classification and Identification Algorithms for Criminal Investigation : A Survey. Future Generation Computer Systems, Elsevier, to appear.
DOI: 10.1016/j.future.2019.10.019		 EISCICAS Q2CCF-CIF: 5.768
142. Wu. J. M. T, Lin, J. C.-W., Fournier-Viger, P. Dj, Y., Chen, C.-H., Li, Z.(2019). Density-based Clustering Method for Privacy-Preserving Data Mining. Mathematical Biosciences and Engineering (MBE), 11 pages.
DOI: ___		 SCICAS Q4JCR Q3IF: 1.230
2018 143. Duong, H., Ramampiaro, H., Norvag, K., Fournier-Viger, P., Dam, T.-L. (2018). High Utility Drift Detection in Quantitative Data Streams. Knowledge-Based Systems (KBS), Elsevier, 157 (1): 34-51.
DOI: 10.1016/j.knosys.2018.05.014		 EISCICAS Q2JCR Q1CCF-CIF: 5.3
144. Gan, W., Lin, J. C.-W., Fournier-Viger, P., Chao, H.-C., Fujita, H. (2018). Extracting non-redundant correlated purchase behaviors by utility measure. Knowledge-Based Systems (KBS), Elsevier, 143: 30-41.
DOI: 10.1016/j.knosys.2017.12.003		 EISCICAS Q2JCR Q1CCF-CIF: 5.3
145. Mafarja, M., Aljarah, I., Heidari, A. A., Faris, H., Fournier-Viger, P., Li, X., Mirjalili, S. (2018). Binary Dragonfly Optimization for Feature Selection using Time-Varying Transfer functions. Knowledge-Based Systems (KBS), Elsevier, 161(1): 185-204.
DOI: 10.1016/j.knosys.2018.08.003		 EISCICAS Q2JCR Q1CCF-CIF: 5.3
Web-of-Science Highly-Cited paper
146. Lin, C.-W., Ren, S., Fournier-Viger, P., Hong, T.-P. (2018). MEMU: More Efficient Algorithm to Mine High Average-Utility Patterns with Multiple Minimum Average-Utility Thresholds. IEEE Access, 6: 7593-7609.
DOI: 10.1109/ACCESS.2018.2801261		 EISCICAS Q2JCR Q1IF: 4.098
147. W. J. M. T., Lin, J.C.W. Pirouz, M., Fournier-Viger, P. (2018) TUB-HAUPM: Tighter Upper Bound for Mining High Average-Utility Patterns. IEEE Access 6: 18655-18669.
DOI: 10.1109/ACCESS.2018.2820740		 EISCICAS Q2JCR Q1IF: 4.098
148. Fournier-Viger, P., Zhang, Y., Lin, J. C.-W., Dinh, T., Le, B. (2018) Mining Correlated High-Utility Itemsets Using Various Correlation Measures. Logic Journal of the IGPL, Oxford Academic, 28(1): 19-32. [source code]
DOI: 10.1093/jigpal/jzz068		 EISCICAS Q4JCR Q3Corr. AuthorIF: 0.575
149. Zhang, B., Lin, J. C.-W., Fournier-Viger, P., Djenouri, Y. (2018) A (k, p)-anonymity Framework to Sanitize Transactional Database with Personalized Sensitivity. Journal of Internet Technology, Taiwan Academic Network, to appear. EISCICAS Q4JCR Q4IF: 0.715
150. Dinh, D.-T., Le, B., Fournier-Viger, P., Huynh, V.-N. (2018) An efficient algorithm for mining periodic high-utility sequential patterns. Applied Intelligence, 48(12):4694-4714.
DOI: 10.1007/s10489-018-1227-x		 EISCICAS Q3JCR Q2CCF-CIF: 1.983
151. Lin, J. C.-W., Shao, Y., Fournier-Viger, P., Djenouri, Y. (2018) Maintenance algorithm for high average-utility itemsets with transaction deletion. Applied Intelligence, 48(10): 3691-3706
DOI: 10.1007/s10489-018-1180-8		 EISCICAS Q3JCR Q2CCF-CIF: 1.983
152. Le, T., Vo, B., Fournier-Viger, P., Lee, M. Y., Baik, S. W. (2018). SPPC: A New Tree Structure for Mining Erasable Patterns in Data Streams. Applied Intelligence, 459:117-134.
DOI: 10.1007/s10489-018-1280-5		 EISCICAS Q3JCR Q2CCF-CIF: 1.983
153. Djenouri, Y., Djenourni, D., Belhadi, A., Fournier-Viger, P., Lin, J. C.-W. (2018) A New Framework for Metaheuristic-based Frequent Itemset Mining. Applied Intelligence, 48(4775-4791(2018)
DOI: 10.1007/s10489-018-1245-8		 EISCICAS Q3JCR Q2CCF-CIF: 1.983
154. Le, B. Dinh, D.-T., Huynh, N.V., Nguyen, Q. M., Fournier-Viger, P. (2018). An Efficient Algorithm for Hiding High Utility Sequential Patterns. International Journal of Approximate Reasoning, Elsevier, 95:77-92.
DOI: 10.1016/j.ijar.2018.01.005		 EISCICAS Q3JCR Q2CCF-BIF: 2.8
155. Lin, J. C.-W., Ren, S.-F., Fournier-Viger, P., Pan, J.-S., Hong, T.-P. (2018). Efficiently Updating the Discovered High Average-Utility Itemsets with Transaction Insertion. Engineering Applications of Artificial Intelligence (EAAI), Elsevier, 72: 136-149.
DOI: 10.1016/j.engappai.2018.03.021		 EISCICAS Q2JCR Q1CCF-CIF: 2.8
156. Djenouri, Y., Belhadi, A, Fournier-Viger, P., Fujita, H. (2018) Mining Diversified Association Rules in Big Datasets: a Cluster/GPU/Genetic Approach. Information Sciences, Elsevier, to appear.
DOI: 10.1016/j.ins.2018.05.031		 EISCICAS Q2JCR Q1CCF-BIF: 4.305
157. Djenouri, Y., Belhadi, A, Fournier-Viger, P., Lin, J. C.-W. (2018) Fast and Effective Cluster-based Information Retrieval using Frequent Closed Itemsets. Information Sciences, Elsevier, 453: 154-167.
DOI: 10.1016/j.ins.2018.04.008		 EISCICAS Q2JCR Q1CCF-BIF: 4.305
158. Djenouri, Y., Djenouri, D., Belhadi, A, Fournier-Viger, P., Lin, J. C.-W., Bendjoudi, A. (2018) Exploiting GPU parallelism in improving bee swarm optimization for mining big transactional databases. Information Sciences, Elsevier, 496: 326-342.
DOI: 10.1016/j.ins.2018.06.060		 EISCICAS Q2JCR Q1CCF-BIF: 4.305
159. Lin, J.-C.W., Yang, L., Fournier-Viger, P., Hong, T.-P. (2018). Mining of Skyline Patterns by Considering both Frequent and Utility Constraints. Engineering Applications of Artificial Intelligence, Elsevier, 77: 229-238.
DOI: 10.1016/j.engappai.2018.10.010		 EISCICAS Q2JCR Q1CCF-CIF: 2.8* Best theory award EAAI / IFAC 2020* 🏆
160. Lin, J.-C.W., Zhang, Y., Fournier-Viger, P., Hong, T.-P. (2018) Efficiently Updating the Discovered Multiple Fuzzy Frequent Itemsets with Transaction Insertion, International Journal of Fuzzy Systems, Springer, 20(8):2440–2457.
DOI: 10.1007/s40815-018-0520-5		 EISCICAS Q3JCR Q2IF: 2.19
161. Djenouri, Y., Djenouri, D., Belhadi, A, Fournier-Viger, P., Lin, J. C.-W., (2018). GPU-based Swarm Intelligence for Association Rule Mining in Big Databases. Intelligent Data Analysis, IOS Press, 23(1): 57-76.
DOI: 10.3233/IDA-173785		 EISCICAS Q4JCR Q4CCF-CIF: 0.612
2017 162. Gan, W., Lin, J. C.-W., Fournier-Viger, P., Chao, H.-C., Hong, T.-P., Fujita, H. (2017). Incremental High-Utility Itemset Mining: A Survey. WIREs Data Mining and Knowledge Discovery, Wiley, to appear.
DOI: 10.1002/widm.1242		 EISCICAS Q3JCR Q2
163. Fournier-Viger, P., Lin, J. C.-W., Vo, B, Chi, T.T., Zhang, J., Le, H. B. (2017). A Survey of Itemset Mining. WIREs Data Mining and Knowledge Discovery, Wiley, e1207 doi: 10.1002/widm.1207, 18 pages. EISCICAS Q3JCR Q2Corr. Author
164. Fournier-Viger, P., Lin, J. C.-W., Kiran, R. U., Koh, Y. S., Thomas, R. (2017). A Survey of Sequential Pattern Mining. Data Science and Pattern Recognition (DSPR), vol. 1(1), pp. 54-77. Corr. Author
165. Lin, J. C. W, Ren, S., Fournier-Viger, P. Hong, T.-P., Su, J.-W., Vo, B. (2017). A Fast Algorithm for Mining High Average-Utility Itemsets. Applied Intelligence, Springer, 47:331-347.
DOI: 10.1007/s10489-017-0896-1		 EISCICAS Q3JCR Q2CCF-CIF: 1.983
166. Bouakkaz, M., Ouinten, Y., Loudcher, S., Fournier-Viger, P. (2017). Efficiently mining frequent itemsets applied for textual aggregation. Applied Intelligence, Springer, 48(4):1013-1019).
DOI: 10.1007/s10489-017-1050-9		 EISCICAS Q3JCR Q2CCF-CIF: 1.983
167. Nguyen, L. T. T., Vo, B., Nguyen, L. T. T., Fournier-Viger, P., Selamat, A. (2017). ETARM: an efficient top-k association rule mining algorithm. Applied Intelligence, Springer, vol. 48, no. 5, 1148-1160.
DOI: 10.1007/s10489-017-1047-4		 EISCICAS Q3JCR Q2CCF-CIF: 1.983
168. Duong, Q.H., Fournier-Viger, P., Ramampiaro, H., Norvag, K. Dam, T.-L. (2017). Efficient High Utility Itemset Mining using Buffered Utility-Lists . Applied Intelligence, Springer, 48(7), pp. 1859–1877.
DOI: 10.1007/s10489-017-1057-2		 EISCICAS Q3JCR Q2CCF-CCorr. AuthorIF: 1.983
169. Zhang, J., Zhu, X., Fournier-Viger, P., Lin, J. C.-W. (2017). FRIOD: A Deeply Integrated Feature-Rich Interactive System for Effective and Efficient Outlier Detection,. IEEE Access (to appear)
DOI: 10.1109/ACCESS.2017.2771237		 EISCICAS Q2JCR Q1IF: 4.098
170. Lin, C.-W., Ren, S., Fournier-Viger, P., Hong, T.-P. (2017). EHAUPM: Efficient High Average-Utility Pattern Mining with Tighter Upper-Bounds. IEEE Access, 5:12927-12940.
DOI: 10.1109/ACCESS.2017.2717438		 EISCICAS Q2JCR Q1IF: 4.098
171. Lin, C.-W., Liu, Q., Fournier-Viger, P., Hong, T.-P. (2017). PTA: An Efficient System for Transaction Database Anonymization . IEEE Access, vol. 4, pp. 6467-6479, IEEE.
DOI: 10.1109/ACCESS.2016.2596542		 EISCICAS Q2JCR Q1IF: 4.098
172. Djenouri, Y., Habbas, Z., Djenouri, D., Fournier-Viger, P. (2017). Bee Swarm Optimization For Solving MAXSAT Problem Using Prior Knowledge. Soft Computing, Springer, 496: 326-342.
DOI: 10.1007/s00500-017-2956-1		 EISCICAS Q3JCR Q2CCF-CIF: 2.7
173. Djenouri, Y., Belhadi, A., Fournier-Viger, P. Extracting Useful Knowledge from Event Logs: A Frequent Itemset Mining Approach (2017). Knowledge-Based Systems (KBS), Elsevier,139: 132-148.
DOI: 10.1016/j.knosys.2017.10.016		 EISCICAS Q2JCR Q1CCF-CCorr. AuthorIF: 4.396
174. Lin, J. C.-W., Gan, W., Fournier-Viger, P., Chao, H.-C., Wu. J. T., Zhan, J. (2017). Extracting Recent Weighted-based Patterns from Uncertain Temporal Databases. Engineering Applications of Artificial Intelligence, Elsevier, 61, pp. 161-172
DOI: 10.1016/j.engappai.2017.03.004		 EISCICAS Q2JCR Q1CCF-CIF: 2.8
175. Lin, J. C.-W., Gan, W., Fournier-Viger, P., Chao, H.-C., Zhan, J. (2017). Mining of Frequent Patterns with Multiple Minimum Supports. Engineering Applications of Artificial Intelligence, Elsevier, 60:83-96.
DOI: 10.1016/j.engappai.2017.01.009		 EISCICAS Q2JCR Q1CCF-CIF: 2.8
176. Gan, W., Lin, J. C.-W., Fournier-Viger, P., Chao, H.-C., Zhan, J., Zhang, J. (2017). Exploiting highly qualified pattern with frequency and weight occupancy. Knowledge and Information Systems (KAIS), Springer, 56(1):165-196.
DOI: 10.1007/s10115-017-1103-8		 EISCICAS Q3JCR Q2CCF-BIF: 2.247
177. Le, B., Duong, H., Truong, T., Fournier-Viger, P. (2017). FCloSM, FGenSM: Two New Algorithms for Efficiently Mining Frequent Closed and Generator Sequences using Local Pruning Strategy. Knowledge and Information Systems (KAIS), Springer, 53(1):71-107.
DOI: 10.1007/s10115-017-1032-6		 EISCICAS Q3JCR Q2CCF-BIF: 2.247
178. Dam, T.-L., Li, K., Fournier-Viger, P., Duong, H. (2017). An efficient algorithm for mining top-k on-shelf high utility itemsets. Knowledge and Information Systems (KAIS), Springer, 62:621-655.
DOI: 10.1007/s10115-016-1020-2		 EISCICAS Q3JCR Q2CCF-BIF: 2.247
179. Zida, S., Fournier-Viger, P., Lin, J. C.-W., Wu, C.-W., Tseng, V.-S. (2017). EFIM: A Fast and Memory Efficient Algorithm for High-Utility Itemset Mining . Knowledge and Information Systems (KAIS), Springer, 51(2), 595-625 [short powerpoint - long powerpoint] [source code]
DOI: 10.1007/s10115-016-0986-0		 EISCICAS Q3JCR Q2CCF-BCorr. AuthorIF: 2.247
180. Lin, J. C., Hong, T.-P., Fournier-Viger, P., Liu, Q., Wong, J.-W., Zhan, J. (2017). Efficient Hiding of Confidential High-Utility Itemsets with Minimal Side Effects.. Journal of Theoretical and Experimental Artificial Intelligence, Taylor and Francis (to appear)
DOI: 10.1080/0952813X.2017.1328462		 EISCI
181. Dam, T.-L., Li, K., Fournier-Viger, P., Duong, H. (2017). CLS-Miner: Efficient and Effective Closed High utility Itemset Mining. Frontiers of Computer Science, Springer (to appear), 13(2): 357-381
DOI: 10.1007/s1170		 EISCICAS Q4JCR Q3CCF-CIF: 1.105
182. Lin, J. C.-W., Zhang, J. Fournier-Viger, P., Hong, T.-P.(2017). A Two-Phase Approach to Mine Short-Periodic High Utility Itemsets in Transactional Databases. Advanced Engineering Informatics, Elsevier, 33, 29-43.
DOI: 10.1016/j.aei.2017.04.007		 EISCICAS Q2JCR Q2CCF-B
183. Amirat, H., Lagraa, N., Fournier-Viger, P., Ouinten, Y. (2017). MyRoute: a Graph-dependency Based Model for Real-time Route Prediction. Journal of Communications, Engineering and Technology Publishing, vol. 12, no. 12, 668-676. 10.12720/jcm.12.12.668-676
DOI: 10.12720/jcm.12.12.668-676.		 EISCI
184. Lin, J. C.-W., Li, T., Fournier-Viger, P., Zhang, J. (2017). Mining of High Average-Utility Patterns with Item-Level Thresholds. Journal of Internet Technology, Taiwan Academic Network (to appear).
DOI:		 EISCICAS Q4JCR Q4IF: 0.715
185. Zhang, B., Lin, J. C.-W., Li, T., Gan, W., Fournier-Viger, P. (2017). Mining High Utility-Probability Sequential Patterns in Uncertain Databases. PLoS One, Public Library of Science, to appear.
DOI: 10.1371/journal.pone.0180931		 EISCI
186. Mustafa, R. U., Nawaz, M. S., Ferzund, J., Lali, M. I. U., Shazad, B., Fournier-Viger, P. (2017). Early Detection of Controversial Urdu Speeches from Social Media. Data Science and Pattern Recognition (DSPR), vol. 1(2), 26-42.
2016 187.

Tseng, V., Wu, C., Fournier-Viger, P., Yu, P. S. (2016). Efficient Algorithms for Mining Top-K High Utility Itemsets. IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(1): 54-67. [source code]
DOI: 10.1109/TKDE.2015.2458860

 EISCICAS Q2JCR Q1CCF-AIF: 2.07
188. Dougnon, Y. R., Fournier-Viger, P., Lin, J. C.-W., Nkambou, R. (2016). Inferring Social Network User Profiles using a Partial Social Graph. Journal of Intelligent Information Systems (JIIS), Springer, 47(2): 313-344.
DOI: 10.1007/s10844-016-0402-y		 EISCICAS Q4JCR Q3CCF-CCorr. Author IF: 1.294
189. Lin, J. C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P. Chao, H.-C. (2016). FDHUP: Fast Algorithm for Mining Discriminative High Utility Patterns . Knowledge and Information Systems (KAIS), Springer, June 2017, Volume 51, Issue 3, 873-909
DOI: 10.1007/s10115-016-0991-3		 EISCICAS Q3JCR Q2CCF-BIF: 2.004
190. Lin, J. C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P., Tseng, V. S. (2016). Efficiently Mining Uncertain High-Utility Itemsets. Soft Computing, Springer, 21:2801-2820.
DOI: 10.1007/s00500-016-2159-1		 EISCICAS Q3JCR Q2CCF-CIF: 2.47
191. Lin, J. C.-W., Yang, L., Fournier-Viger, P., Hong, T.-P., Voznak, M. (2016). A binary PSO approach to mine high-utility itemsets. Soft Computing, Springer. 21(17): 5103-5121
DOI: 10.1007/s00500-016-2106-1		 EISCICAS Q3JCR Q2CCF-CIF: 2.47
192. Dam, T.-L., Li, K. , Fournier-Viger, P. (2016). Chemical reaction optimization with unified tabu search for the vehicle routing problem. Soft Computing, Springer, 21(21): 6421-6433
DOI: 10.1007/s00500-016-2200-4		 EISCICAS Q3JCR Q2CCF-CIF: 2.47
193. Lin, J. C.-W., Liu, Q., Fournier-Viger, P., Hong, T.-P., Voznak, M., Zhan, J. (2016). A Sanitization Approach For Hiding Sensitive Itemsets Based On Particle Swarm Optimization. Engineering Applications of Artificial Intelligence, 53:1-18.
DOI: 10.1016/j.engappai.2016.03.007		 EISCICAS Q2JCR Q1CCF-CIF: 2.894
194. Lin, J. C.-W., Li, T., Fournier-Viger, P., Hong, T.-P., Voznak, M., Zhan, J. (2016). An Efficient Algorithm to Mine High Average-Utility Itemsets. Advanced Engineering Informatics, Elsevier, 30(2): 233-243.
DOI: 10.1016/j.aei.2016.04.002		 EISCICAS Q2JCR Q2CCF-BIF: 2.68
195. Lin, J. C. W., Gan, W., Fournier-Viger, P., Hong, T. P., Tseng, V. S. (2016). Weighted frequent itemset mining over uncertain databases. Applied Intelligence, 44:232-250.
DOI: 10.1007/s10489-015-0703-9		 EISCICAS Q3JCR Q2CCF-CIF: 1.904
196. Dam, T.-L., Li, K., Fournier-Viger, P. (2016). An efficient algorithm for mining top-rank-k frequent patterns. Applied Intelligence, Springer, 45(1): 96-111.
DOI: 10.1007/s10489-015-0748-9		 EISCICAS Q3JCR Q2CCF-C
197. Lin, J. C. W., Gan, W., Fournier-Viger, P., Hong, T. P., Tseng, V. S. (2016). Fast Algorithms for Mining High-Utility Itemsets with Various Discount Strategies. Advanced Engineering Informatics, 30(2): 109-126.
DOI: 10.1016/j.aei.2016.02.003		 EISCICAS Q2JCR Q2CCF-BIF: 2.68
198. Lin, J. C. W., Fournier-Viger, Gan, W. (2016). FHN: An Efficient Algorithm for Mining High-Utility Itemsets with Negative Unit Profits. Knowledge-Based Systems (KBS), Elsevier, 111(1):283-298
DOI: 10.1016/j.knosys.2016.08.022		 EISCICAS Q2JCR Q1CCF-CCorr. AuthorIF: 4.529
199. Lin, J. C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P., Zhan, J. (2016). Efficient Mining of High-Utility Itemsets Using Multiple Minimum Utility Thresholds. Knowledge-Based Systems (KBS), Elsevier, 113:100-115.
DOI: 10.1016/j.knosys.2016.09.013		 EISCICAS Q2JCR Q1CCF-CIF: 4.529
200. Duong, Q.-H., Liao, B., Fournier-Viger, P., Dam, T.-L. (2016). An efficient algorithm for mining the top-k high utility itemsets, using novel threshold raising and pruning strategies. Knowledge-Based Systems (KBS), Elsevier, 104:106-122.
DOI: 10.1016/j.knosys.2016.04.016		 EISCICAS Q2JCR Q1CCF-CIF: 4.529
201.

Lin., J. C. W., Gan. W., Fournier-Viger, P., Tseng, V. S. (2016). Efficient Algorithms for Mining High-Utility Itemsets in Uncertain Databases. Knowledge-Based Systems (KBS), Elsevier, 96, 171-187.
DOI: 10.1016/j.knosys.2015.12.019

 EISCICAS Q2JCR Q1CCF-CIF: 4.529
202. Lin., J. C. W., Gan. W., Fournier-Viger, P., Hong, T. P. (2016). Efficiently Updating the Discovered Sequential Patterns for Sequence Modification. Int'l Journal of Software Engineering and Knowledge Engineering (IJSEKE), 26(8), pp 1285-1313.
DOI: 10.1142/S0218194016500455		 EISCICAS Q4CCF-CIF: 4.529
2015 203. Fournier-Viger, P., Wu, C.-W., Tseng, V.S., Cao, L., Nkambou, R. (2015). Mining Partially-Ordered Sequential Rules Common to Multiple Sequences. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(8): 2203-2216. [source code] EISCICCF-ACorr. AuthorIF: 2.07
204. Tseng, V., Wu, C., Fournier-Viger, P., Yu, P. S. (2015). Efficient Algorithms for Mining the Concise and Lossless Representation of Closed+ High Utility Itemsets. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(3): 726-739. [source code] EISCICCF-AIF: 2.07
205. Lin., J. C. W., Yang, L., Fournier-Viger, Frnda, J., Sevcik, L., Voznak, M. (2015). An Evolutionary Algorithm to Mine High-Utility Itemsets. Advances in Electrical and Electronic Engineering, Vol. 13, No.5., pp. 392-398. EISCICCF-B
206. Lin, J. C. W., Tin, L., Fournier-Viger, P., Hong, T. P. (2015). A fast Algorithm for mining fuzzy frequent itemsets. Journal of Intelligent and Fuzzy Systems, 29(6), 2373-2379. EISCIIF: 1.81
207. Lin, J. C., Gan, W., Fournier-Viger, P., Hong, T.-P. (2015). RWFIM: Recent Weighted-Frequent Itemsets Mining. Engineering Applications of Artificial Intelligence, Elsevier, 45, 18-32. EISCICAS Q2JCR Q1CCF-CIF: 2.21
2014 208. Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu., C., Tseng, V. S. (2014). SPMF: a Java Open-Source Pattern Mining Library. Journal of Machine Learning Research (JMLR), 15: 3389-3393. EISCICCF-ACorr. AuthorIF: 2.47
2013 209. Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E., Mayers, A., Faghihi, U. (2013). A Multi-Paradigm Intelligent Tutoring System for Robotic Arm Training. IEEE Transactions on Learning Technologies (TLT), 6(4): 364-377. EISSCICorr. AuthorIF: 1.22
2012 210. Fournier-Viger, P., Faghihi, U., Nkambou, R., Mephu Nguifo, E. (2012). CMRules: Mining Sequential Rules Common to Several Sequences. Knowledge-based Systems, Elsevier, 25(1): 63-76. [source code] EISCICCF-CIF: 4.104
211. Faghihi, U., P. Fournier-Viger, Nkambou, R. (2012). A Computational Model for Causal Learning in Cognitive Agents, Knowledge-Based Systems, Elsevier, 30, 48-56 EISCICCF-CIF: 4.104
2011 212. Faghihi, U., Poirier, P., Fournier-Viger, P., Nkambou, R. (2011). Human-Like Learning in a Cognitive Agent. Journal of Experimental & Theoretical Artificial Intelligence, Taylor & Francis, 23(4): 497-528. EISCIIF: 0.33
213. Nkambou, R., Fournier-Viger, P., Mephu Nguifo, E. (2011). Learning Task Models in Ill-defined Domain Using an Hybrid Knowledge Discovery Framework. Knowledge-Based Systems, Elsevier, 24(1):176-185. EISCICCF-CIF: 2.42
<2010 214. Fournier-Viger, P., Faghihi, U., Nkambou, R., Mephu Nguifo, E. (2010). Exploiting Sequential Patterns Found in Users’ Solutions and Virtual Tutor Behavior to Improve Assistance in ITS. Educational Technology & Society, 13(1):12-24.
DOI:		 SSCICorr. AuthorIF: 1.34
215. Nkambou, R, Fournier-Viger, P., Mephu Nguifo, E. (2009). Improving the Behavior of Intelligent Tutoring Agents with Data Mining. IEEE Intelligent Systems, 24(3):46-53.
DOI: 10.1109/MIS.2009.59		 EISCIIF: 3.14
216. Fournier-Viger, P., Nkambou, R., Mayers, A. (2008). Evaluating Spatial Representations and Skills in a Simulator-Based Tutoring System. IEEE Transactions on Learning Technologies (TLT), 1(1):63-74.
DOI: 10.1109/TLT.2008.13		 EISSCICorr. AuthorIF: 0.82
217. Fournier-Viger, P., Najjar, M., Mayers, A., Nkambou, R. (2006). A Cognitive and Logic based Model for Building Glass-box Learning Objects. Interdisciplinary Journal of e-Skills and Lifelong Learning, 2:77-94.
DOI: 10.28945/402		 Corr. AuthorIF: 22

Conference Papers


2026 1. Zhao, D., Zhang, J., Hu, H., Fournier-Viger, P., Dobbie, G., Koh, Y. (2026). Unlearning during Training: Domain-Specific Gradient Ascent for Domain Generalization. Proceedings of 2026 International Conference on Learning Representations (ICLR 2026). to appear. CCF-A
2025 2. Govan, R., Scherrer, R., Goarant, C., Cannet, A., Fournier-Viger, P., Selmaoui-Folcher, N.(2025). Cartographie épidémiologique : Le défi des données déséquilibrées. Proc. 25ème conférence Interne sur l'extraction et la gestion des connaissances (EGC 2025), Revue des Nouvelles Technologies de l'Information, pp. 159-170.
3. Zhao, D., Dobbie, G., Zhang, J., Hu, H., Fournier-Viger, P., Koh, Y.S. (2025). Balancing Invariant and Specific Knowledge for Domain Generalization with Online Knowledge Distillation. Proc. of the 35th International Joint Conference on Artificial Intelligence (IJCAI 2025). pp. 2440-2448. EICCF-A
4. Govan, R., Scherrer, R., Fournier-Viger, P., Selmaoui-Folcher, N. (2025).SpaPool: Soft Partition Assignment Pooling for Graph Neural Networks. Proc. 27th Intern. Conf. on Data Warehousing and Knowledge Discovery (DAWAK 2025), Springer, LNCS, pp. 332-340. EI
5. Nawaz, M. Z., Nawaz, S., Fournier-Viger, P, Niu, X., Li, M. (2025). A Multipurpose Protein Compressor based on MDL and Genetic Algorithm. Proceedings of BIBM 2025,to appear. CCF-BCorr. Author
6. Wang, Z., Hu, Y., Fu, Y., Wu, Z., Cao, J., Liu, X., Fournier-Viger, P. (2025). G-Shapelets: Predicting Longitudinal Gene Expression Dynamics via Interpretable Multi-Scale Temporal Motifs. Proceedings of BIBM 2025, 6724-6731. CCF-B
7. Govan, R., Scherrer, R., Fournier-Viger, P., Selmaoui-Folcher, N. (2025) Pooling de Graph Neural Networks : une approche dense mais adaptative. Conférence Nationale en Intelligence Artificielle / French National AI conference (CNIA). to appear
8. Win, K. N., Zhu, X., Fournier-Viger, P. (2025) SAFE: A Blockchain-Based Framework for Secure Health Data Sharing with Symmetric Encryption. Proc. of BlockSys 2025, Springer, to appear
2024 9. Zhao, D., Koh, Y. S., Dobbie, G., Hu, H., Fournier-Viger, P. (2024). Symmetric Self-Paced Learning for Domain Generalization. Proc. 38th AAAI Conference on Artificial Intelligence (AAAI 2024). 16961-16969 EICCF-A
10. Nawaz, S., Fournier-Viger, P, Wu, J. W.-M. (2024). SeqClin: Pattern-Based Analysis and Classification of Clinical Datasets. Proceedings of BIBM 2024, 6490-6497. CCF-BCorr. Author
11. Nawaz, M. Z., Nawaz, S., Fournier-Viger, P, Tseng, V.-S. (2024). An MDL-Based Genetic Algorithm for Genome Sequence Compression. Proceedings of BIBM 2024, 6724-6731. CCF-BCorr. Author
2023 12. Zhi, C., Andriamampianina, L., Ravat, F., Song, J., Valles-Parlangeau, N., Fournier-Viger, P., Selmaoui-Folcher, N. (2023) Mining Frequent Sequential Subgraph Evolutions in Dynamic Attributed Graphs. Proc. 27th Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD 2023), Springer, to appear EICCF-C
accept. rate: 17 %
13. Govan, R., Selmaoui, N., Giannakos, A., Fournier-Viger, P. (2023). Co-location pattern mining under the spatial structure constraint. Proc. 34th International Conference on Database and Expert Systems Applications (DEXA 2023), Springer, to appear.
DOI:		 EICCF-C
14. Zhang, Q., Shi, Z. L., Zhang, X., Chen, X., Fournier-Viger, P., Pan, S. (2023). G2Pxy: Generative Open-Set node Classification on Graphs with Proxy Unknowns. Proc. of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023). to appear EICCF-A
accept. rate: 15 %
15. Zhang, X., Niu. X., Fournier-Viger, P., Dai, X. (2023) Image-text Retrieval via Preserving Main Semantics of Vision. Proc. 2023 IEEE International Conference on Multimedia and Expo (ICME 2023), to appear. CCF-B
16. Kiran, R. U., ... , Fournier-Viger, P., ...(2023). Discovering Fuzzy Partial Periodic Patterns in Quantitative Irregular Multiple Time Series. Proc. 16th IEEE Conference on Fuzzy Systems (FUZZY-IEEE 2023), IEEE, to appear
DOI:
17. Lekfir, M., Nouioua, F., Fournier-Viger, P. (2023). Heuristic approaches for hiding sensitive frequent itemsets in uncertain databases. Proc. 5th International Conference on Pattern Analysis and Intelligent Systems (PAIS 2023). to appear.
18. Govan, R., Selmaoui-Folcher, N., Giannakos, A., Fournier-Viger, P. (2023) Extraction de co-localisations sous contrainte de la structure spatiale. Actes de la Conférence Nationale en Intelligence Artificielle 2023 (Proc. of the National conference of Artificial Intelligence of France). to appear.
2022 19. Liu, J., Zhou, M., Fournier-Viger, P., Yang, M., Pan, L., Nouioua, M. (2022) Discovering Representative Attribute-stars via Minimum Description Length. Proc. of the 38th IEEE International Conference on Data Engineering (ICDE 2022), 12 pages, to appear. EICCF-A
accept. rate:
20. Nawaz, S., Fournier-Viger, P, He, Y. (2022). S-PDB: Analysis and Classification of SARS-COV2 Spike Protein Structures. Proceedings of IEEE BIBM 2022. CCF-BCorr. Author
21. Zhang, Q., Li, Q., Chen, X., Zhang, P., Pan, S., Fournier-Viger, P., Huang, J. Z. (2022). A Dynamic Variational Framework for Open-World Node Classification in Structured Sequences. Proc. of IEEE International Conference on Data Mining (ICDM 2022), to appear. EICCF-B
accept. rate: 20 %
22. Nawaz, M. S., Nawaz, M. Z., Hasan, O., Fournier-Viger, P. (2022). Metaheuristic Algorithms for Proof Searching in HOL4. The 34th International Conference on Software Engineering & Knowledge Engineering (SEKE 2022). KSI Research Inc., to appear
DOI: 10.18293/SEKE2022-103		 EICorr. Author
23. Ou, G., He, Y. Fournier-Viger, P., Huang, J. Z. (2022). Auto-Encoding Independent Attribute Transformation for Naive Bayesian Classifier. International Joint Conference on Neural Networks (IJCNN 2022), to appear EICCF-C
24. Zhao, D., Koh, Y. S., Fournier-Viger, P. Measuring Drift Severity by Tree Structure Classifiers (2022). International Joint Conference on Neural Networks (IJCNN 2022), to appear EICCF-C
25. Palla, L., Rage, V., Kiran, U., Zettsu, K., Toyoda, M., Fournier-Viger, P. (2022) UPFP-growth++: An Efficient Algorithm to Find Periodic-Frequent Patterns in Uncertain Temporal Databases. Proc. 29th International Conference on Neural Information Processing (ICONIP 2022). Springer, LNCS.
DOI:		 EICCF-C
26. Yin, Z., Gan W., Huang, G., Wu, Y., Fournier-Viger., P. (2022). Constraint-based sequential rule mining . Proc. 2022 International Conference on Data Science and Advanced Analytics (DSAA’2022), to appear. CCF-C
27. Nawaz, M. S., Noor, S., Fournier-Viger, P. (2021). Reasoning about Order Crossover in Genetic Algorithms. Proc. 13th Intern. Conf. on Swarm Intelligence (ICSI 2022), Springer LNCS, 11 pages, to appear.
DOI: 10.1007/978-3-031-09677-8_22
 EICorr. Author
28. Nawaz, M. S., Fournier-Viger, P., Alhusaini, N., He, Y., Wu, Y. and Bhattacharya, D. (2022). LCIM: Mining Low Cost High Utility Itemsets . Proc. of the 15th Multi-disciplinary International Conference on Artificial Intelligence (MIWAI 2022), pp. 73-85, Springer LNAI [video] [ppt]. [source code and data]

EICorr. Author

* Best paper award* 🏆
29. Fournier-Viger, P., Nawaz, M. S., He, Y., Wu, Y., Nouioua, F., Yun, U. (2022). MaxFEM: Mining Maximal Frequent Episodes in Complex Event Sequences. Proc. of the 15th Multi-disciplinary International Conference on Artificial Intelligence (MIWAI 2022), pp. 86-98, Springer LNAI. [source code] [ppt]

EI

* Best presentation award* 🏆
30. Fournier-Viger, P. Li, Y., Nawaz, M. S., He, Y. (2022) FastTIRP: Efficient discovery of Time-Interval Related Patterns. Proc. of 10th Intern. Conf. on Big Data Analytics (BDA 2022), Springer, to appear. EICorr. Author
31. Belghith, K., Fournier-Viger, P., Jawadi, J. (2022). Hui2Vec: Learning Transaction Embedding Through High Utility Itemsets. Proc. of 10th Intern. Conf. on Big Data Analytics (BDA 2022), Springer, to appear. EI
32. Zhang, X., Niu, X., Fournier-Viger, P. Wang, B. (2022). Two-stage Traffic Clustering Based on HNSW. Proc. 35th Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2022), LNAI, 12 pages.
DOI: 10.1007/978-3-031-08530-7_51		 EI
33. Fan, G., Xiao, H., Zhang, C. Almpanidis, G., Fournier-Viger, P., Fujita, H. (2022). Parallel High Utility Itemset Mining. Proc. 35th Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2022), LNAI, 12 pages.
DOI: 10.1007/978-3-031-08530-7_69		 EI
2021 34. Fournier-Viger, P., Chen, Y., Nouioua, F., Lin, J. C.-W. (2021). Mining Partially-Ordered Episode Rules in an Event Sequence. Proc. of the 13th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2021), Springer LNAI, pp 3-15 [video] [software] [ppt] [source code]

* Best paper award* 🏆

EICorr. Author
35. Nawaz, M. S., Fournier-Viger, P., Song, W., Lin, J. C.-W., Noack, B. (2021). Investigating Crossover Operators in Genetic Algorithms for High-Utility Itemset Mining. Proc. of the 13th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2021), Springer LNAI, pp. 16-28. EICorr. Author
36.

Song, W., Zheng, C., Fournier-Viger, P. (2021). Mining Skyline Frequent-Utility Itemsets with Utility Filtering. Proc. 18th Pacific Rim Intern. Conf on Artificial Intelligence (PRICAI-2021), Springer LNCS, to appear.

EICCF-C
37. Hossain, M., Wu, Y., Fournier-Viger, P., Li, Z., Guo, L, Li, Y. (2021). HSNP-Miner: High Utility Self-Adaptive Nonoverlapping Pattern Mining. Proc. 12th IEEE Conference on Big Knowledge, IEEE, 8 pages, to appear.
38. Huynh, H.-T., Duong, H., Le, B., Fournier-Viger, P. (2021). Mining High Utility Sequences with a Novel Utility Function. Proc. of 13th IEEE International Conference on Knowledge and Systems Engineering (KSE 2021), IEEE, to appear * Nominated for best paper award
(in top 3) *
39. Chen, Y., Fournier-Viger, P., Nouioua, F., Wu, Y. (2021). Mining Partially-Ordered Episode Rules with the Head Support. Proc. 23rd Intern. Conf. on Data Warehousing and Knowledge Discovery (DAWAK 2021), Springer, LNCS, 7 pages
DOI: https://doi.org/10.1007/978-3-030-86534-4_26 [ppt] [video] [source code]		 EICorr. Author
40. Nawaz, M. S., Sun, M., Fournier-Viger, P. (2021). Proof Searching in PVS theorem prover using Simulated Annealing. Proceedings of the 12th Intern. Conf. on Swarm Intelligence (ICSI 2021), Springer LNCS, pp. 253-262. [powerpoint] EICorr. Author
41. Ouarem, O., Nouioua, F., Fournier-Viger, P. (2021). Mining Episode Rules From Event Sequences Under Non-Overlapping Frequency. Proc. 34th Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2021), Springer LNAI, pp. 73-85. [source code] EI
42. Hackman, A., Huang, Y., Fournier-Viger, P., Tseng, V.-S. (2021). Stable High Utility Itemset Mining. Proc. the 23rd International Conference on Information Integration and Web Intelligence (iiWAS2021), ACM, 12 pages, to appear. EI
43. Nouioua, M., Fournier-Viger, P., Gan, W., Wu, Y., Lin, J. C.-W., Nouioua, F. (2021). TKQ: Top-K Quantitative High Utility Itemset Mining. Proc. 16th Intern. Conference on Advanced Data Mining and Applications (ADMA 2021) Springer LNAI, 12 pages [ppt] [poster] [video] [source code] EICorr. Author
* Best poster runner-up award*
44. Kiran, R. U., Pallikila, P., Luna, J. M., Fournier-Viger, P., Toyoda, M., Reddy, P. K. Discovering Relative High Utility Itemsets Using Null-Invariant Measure. Proc. 2021 Int. Conf. on Big Data (IEEE BigData 2021), IEEE, 10 pages.
45. Chen, D., Niu, X., Fournier-Viger, P., Wu, W., Wang, B. (2021). Map-matching based on HMM for Urban Traffic. Proc. 34th Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2021), Springer LNAI, pp. 462-473. EI
46. Nawaz, M. S., Fournier-Viger, P., Niu, X., Wu, Y., Lin, J. C.-W. (2021). COVID-19 Genome Analysis using Alignment-Free Methods. Proc. 34th Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2021), Springer LNAI, pp. 316-328 EICorr. Author
47. Srivastava, G, Lin, J.-W., Fournier-Viger, P. (2021). A Transaction Classification Model of Federated Learning. Proc. 34th Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2021), Springer LNAI, pp. 509-518. EI
48. Ahmed, U., Lin, J. C.-W., Fournier-Viger, P., Cheng, C.-F. (2021). Privacy-Preserving Periodic Frequent Pattern Model in AIoT Applications. Proc. 2021 International Symposium on Intelligent Signal Processing and Communication Systems, 2 pages, to appear.
2020 49. Fournier-Viger, P., He, G., Lin, J. C.-W., Gomes, H. M. (2020). Mining Attribute Evolution Rules in Dynamic Attributed Graphs. Proc. 22nd Intern. Conf. on Data Warehousing and Knowledge Discovery (DAWAK 2020), Springer, pp. 167-182. [ppt]
DOI: 10.1007/978-3-030-59065-9_14		 EICorr. Author
50. Nawaz, M. Z., Hasan, O., Nawaz, S., Fournier-Viger, P., Sun, M. (2020). Proof Searching in HOL4 with Genetic Algorithm. Proc. 35th Symposium on Applied Computing (ACM SAC 2020). ACM Press, pp. 513-520.
DOI: 10.1145/3341105.3373917		 EI
51. Fournier-Viger, P., Yang, Y., Lin, J. C.W., Frnda, J. (2020). Mining Locally Trending High Utility Itemsets. Proc. 24th Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD 2020), Springer, LNAI, pp.99-111. [video] [ppt] [source code]
DOI: 10.1007/978-3-030-47436-2_8		 EICCF-CCorr. Author
accept. rate: 21 %
52. Fournier-Viger, P., Wang, Y., Yang, P., Lin, J. C.-W., Yun, U. (2020). TKE: Mining Top-K Frequent Episodes. Proc. 33rd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2020), Springer LNCS , pp. 832-845. [source code] [ppt]
DOI: 10.1007/978-3-030-55789-8_71		 EICorr. Author
53. Fournier-Viger, P., Yang, Y., Lin, J. C.-W., Luna, J. M., Ventura, S. (2020). Mining Cross-Level High Utility Itemsets. Proc. 33rd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2020), Springer LNAI, pp. 858-871. [source code][ppt]
DOI: 10.1007/978-3-030-55789-8_73		 EICorr. Author
54. Zheng, Y., Niu, X., Fournier-Viger, P., Li, F., Gao, L. (2020). Distributed Density Peak Clustering of Trajectory Data on Spark. Proc. 33rd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2020), Springer LNAI, pp. 792-806.
DOI: 10.1007/978-3-030-55789-8_68		 EI
55. Win, K., Chen, J., Mingxin, D., Xiao, G., Li, K., Fournier-Viger, P. (2020). A Decision Support System to Provide Criminal Pattern Based Suggestions to Travelers. Proc. 33rd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2020), Springer LNAI, pp. 582-587.
DOI: 10.1007/978-3-030-55789-8_50		 EI
56. Wu, J.-T., Lin, J C.-W. , Fournier-Viger, P., Cheng, C.-F. (2020). Maintenance of Prelarge High Average-Utility Patterns in Incremental Databases. Proc. 33rd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2020), Springer LNAI, pp. 884-898.
DOI: 10.1007/978-3-030-55789-8_75		 EI
57. Wu. C.-W., Hang, T.-Z., Fournier-Viger, P., Chen, M.-T., Lin, Y.-W. (2020). An Efficient Conversation Group Inference System based on Keywords. Proceedings of 25th International Computer Symposium (ICS 2020). IEEE, to appear. EI
58. Kiran, R. U., Srivasthava, S., Fournier-Viger, P., Zettsu, K., Toyoda, M., Kitsuregawa, M. (2020). Discovering Frequent Neighborhood Patterns in Very Large Spatiotemporal Databases. Proc. ACM 28th Intern. Conf. on Advances in Geographic Information Systems (SIGSPATIAL 2020), to appear.
DOI:		 EI
2019 59. Fournier-Viger, P., Li, J., Lin, J. C.-W., Chi, T.T. (2019). Discovering and Visualizing Patterns in Cost/Utility Sequences. Proc. 21st Intern. Conf. on Data Warehousing and Knowledge Discovery (DAWAK 2019), Springer, pp. 73-88.
DOI: 10.1007/978-3-030-27520-4_6 [source code] 		EICorr. Author
60. Fournier-Viger, P., Cheng, C., Cheng, Z., Lin, J. C.-W., Selmaoui-Folcher, N. (2019). Finding Strongly Correlated Trends in Dynamic Attributed Graphs. Proc. 21st Intern. Conf. on Data Warehousing and Knowledge Discovery (DAWAK 2019), Springer, pp. 250-265.[source code] [ppt]
DOI: 10.1007/978-3-030-27520-4_18		 EICorr. Author
61. Gomez, H. M., Bifet, A., Fournier-Viger, P., Granatyr, J., Read, J. (2019). Network of Experts: Learning from evolving data streams through network-based ensembles. Proc. 26th International Conference on Neural Information Processing (ICONIP 2019). Springer, LNCS 11953, pp. 704–716,
DOI: 10.1007/978-3-030-36708-4_58		 EICCF-C
62. Ktistakis, R., Fournier-Viger, P., PuglS., Raman, R. (2019). Succinct BWT-based Sequence prediction. Proc. 30th International Conference on Database and Expert Systems Applications (DEXA 2019), Springer, pp. 91-10.
DOI: 10.1007/978-3-030-27618-8_7 [ C++ implementation with Python wrapper (unofficial)] [datasets]

EICCF-C

accept. rate: 20%

63.

Kiran, U., Reddy, T. Y., Fournier-Viger, P., Toyoda, M., Reddy, P. K., Kitsuregawa, M. (2019). Efficiently Finding High Utility-Frequent Itemsets using Cutoff and Suffix Utility. Proc. 23nd Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD 2019), Springer, LNAI, pp. 191-203.
DOI: 10.1007/978-3-030-16145-3_15

 EICCF-C
accept. rate: 24.7 %
64. Fournier-Viger, P., Cheng, C., Lin, J. C.-W., Yun, U., Kiran, U. (2019). TKG: Efficient Mining of Top-K Frequent Subgraphs. Proc. of 7th Intern. Conf. on Big Data Analytics (BDA 2019), Springer, 20 pages, pp. 209-226. [ppt] [source code]
DOI: 10.1007/978-3-030-37188-3_13		 EICorr. Author
accept. rate: 25%
65. Duong, H., Truong, T., Le, B., Fournier-Viger, P. (2019). An Explicit Relationship between Sequential Patterns and their Concise Representations. Proc. of 7th Intern. Conf. on Big Data Analytics (BDA 2019), Springer, pp. 341-361.
DOI: 10.1007/978-3-030-37188-3_20		 EI
accept. rate: 25%
66. Fournier-Viger, P., Yang, P., Lin, J. C.-W., Yun, U. (2019). HUE-SPAN: Fast High Utility Episode Mining. Proc. 14th Intern. Conference on Advanced Data Mining and Applications (ADMA 2019) Springer LNAI, pp. 169-184. [ppt] [source code]
DOI: 10.1007/978-3-030-35231-8_12

EICorr. Author

accept. rate: 23%
67. Tang, F., Tse, D., Huang, D. T. J., Koh, Y. S., Fournier-Viger, P. (2019). Adaptive Self-Sufficient Itemset Miner for Transactional Data Streams. Proc. 16th Pacific Rim Intern. Conf on Artificial Intelligence (PRICAI-2019), Springer LNCS, to appear.
DOI: 10.1007/978-3-030-29911-8_32		 EICCF-C
68. Fournier-Viger, P., Yang, P., Lin, J. C.-W., Kiran, U. (2019). Discovering Stable Periodic-Frequent Patterns in Transactional Data. Proc. 32nd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2019), Springer LNAI, pp. 230-244. [ppt] [source code] [video]
DOI: 10.1007/978-3-030-22999-3_21		 EICorr. Author
* Best paper award* 🏆
69. Lin, J. C.-W., Wu, J. M.T., Fournier-Viger, P., Hong, T.P., Ting, L. (2019). Efficient Mining of High Average-Utility Sequential Patterns from Uncertain Databases. Proc. 2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2019), IEEE, pp. 1989-1994.
DOI: 10.1109/SMC.2019.8914546
 EICCF-C
70. Kiran, U., Zettsu, K., Toyoda, M., Fournier-Viger, P., Reddy, P. R., Kitsuregawa, M. (2019). Discovering Spatial High Utility Itemsets in Spatiotemporal Databases. Proc. of 31st Intern. Conf. on Scientific and Statistical Database Management (SSDBM 2019), ACM, pp. 49-60
DOI: 10.1145/3335783.3335789		 EICCF-C
71. Wu, J. W.M., Lin, J. C. W., Fournier-Viger, P., Wiktorski, T., Hong, T.P. (2019). A GA-based Framework for Mining High Fuzzy Utility Itemsets. Proc. IEEE BigData 2019, IEEE, pp. 2708-2715.
DOI: 10.1109/BigData47090.2019.9006171		 EI
accept. rate: 19%
72. Lin, J. C. W., Li, Y., Fournier-Viger, P., Djenouri, Y., Wang, S.-L. (2019). Mining High-Utility Sequential Patterns from Big Datasets. Proc. IEEE BigData 2019, IEEE, pp. 2674-2680.
DOI: 10.1109/BigData47090.2019.9005996		 EI
accept. rate: 19%
73. Win, K. N., Chen, J., Xiao, G., Chen, Y., Fournier-Viger (2019) A Parallel Crime Activity Clustering Algorithm based on Apache Spark Cloud Computing Platform. Proc. of 21st IEEE Conferences on High Performance Computing and Communications (HPCC-2019).pp. 68-74.
DOI: 10.1109/HPCC/SmartCity/DSS.2019.00025		 EICCF-C
74. Wu, J. M.-T., Lin, J. C.-W., Fournier-Viger, P., Chen, C.-H., Zhang, Y. et al. (2019). A Swarm-based Data Sanitization Algorithm in Privacy-Preserving Data Mining. Proc. of 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019), pp. 1461-1467.
DOI: 10.1109/CEC.2019.8790271		 EI
75. Djenouri, Y., Djenouri, D., Belhadi, A., Lin, J. C.W., Bendjoudi, A., Fournier-Viger, P. A Novel Parallel Framework for Metaheuristic-based Frequent Itemset Mining. Proc. of 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019). pp. 1439-1445
DOI: 10.1109/CEC.2019.8790116		 EI
76. Lin, J. C.W., ... Fournier-Viger, P., ... (2019). A Project-based PMiner Algorithm in Uncertain Databases. Proc. 2019 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2019), IEEE, pp. 1-5.
DOI: 10.1109/TAAI48200.2019.8959890		 EI
77. Pham, H. Q., Tran, D., Duong, N. B., Fournier-Viger, P., Ngom, A. (2019). Nuclear: An efficient method for mining frequent itemsets based on kernels and extendable sets. Proc. 6th International Conference on Data Mining and Database, 6(9): 69-86.
DOI: 10.5121/csit.2019.90607
78. Nawaz, M. S., Sun, M., Fournier-Viger, P. (2019). Proof Guidance in PVS with Sequential Pattern Mining. Proc. of 9th Intern. Conf. Fundamentals of Software Engineering (FSEN 2019), 15 pages, Springer, LNCS.
DOI: 10.1007/978-3-030-31517-7_4		 EI
2018 79. Fournier-Viger, P., Zhang, Y., Lin, J. C.-W., Fujita, H., Koh, Y.-S. (2018). Mining Local High Utility Itemsets. Proc. 29th International Conference on Database and Expert Systems Applications (DEXA 2018), Springer, pp. 450-460. [source code] [ppt]
DOI: 10.1007/978-3-319-98812-2_41
 EICCF-CCorr. Author
80. Lin, J.-C.W., Fournier-Viger, P., Wu, L., Gan, W., Djenouri, Y., Zhang, J. (2018). PPSF: A privacy-preserving data mining library. Proc. of IEEE International Conference on Data Mining (ICDM 2018) (demo), to appear.
DOI: 10.1109/ICDMW.2018.00208		 EICCF-B
81. Lin, J. C.-W., Zhang, Y. Y., Fournier-Viger, P., Djenouri, Y., Zhang, J. (2018) A Metaheuristic Algorithm for Hiding Sensitive Itemsets. Proc. 29th International Conference on Database and Expert Systems Applications (DEXA 2018), Springer, pp. 492-498
DOI: 10.1007/978-3-319-98812-2_45		 EICCF-C
82. Fournier-Viger, P., Li, Z., Lin, J. C.-W., Fujita, H., Kiran, U. (2018). Discovering Periodic Patterns Common to Multiple Sequences. Proc. 20th Intern. Conf. on Data Warehousing and Knowledge Discovery (DAWAK 2018), Springer, pp. 231-246 [source code] [ppt]
DOI: 10.1007/978-3-319-98539-8_18

EICorr. Author

Acceptance rate: 17%
83. Lin, J. C.-W., Fournier-Viger, P, Liu, Q., Djenouri, Y., Zhang, J. (2018) Anonymization of Multiple and Personalized Sensitive Attributes. Proc. 20th Intern. Conf. on Data Warehousing and Knowledge Discovery (DAWAK 2018), Springer, pp. 204-215.
DOI: 10.1007/978-3-319-98539-8_16		 EI
Acceptance rate: 17%
84. Stirling, M., Koh, Y.S., Fournier-Viger, P., Ravana, S.D. (2018). Concept Drift Detector Selection for Hoeffding Adaptive Trees. Proc. 31st Australasian Joint Conference on Artificial Intelligence (AI 2018), Springer, pp. 730-736.
DOI: 10.1007/978-3-030-03991-2_65		 EI
85. Fournier-Viger, P., Zhang, Y., Lin, J. C.-W., Koh, Y.-S. (2018). Discovering High utility Change Points in Transactional Data. Proc. 13th Intern. Conference on Advanced Data Mining and Applications (ADMA 2018) Springer LNAI, pp. 392-402 [ppt]
DOI: 10.1007/978-3-030-05090-0_33		 EICorr. Author
86. Liu, T., Bai, X., Zhang, D., Cao, X. Fournier-Viger, P., (2018). Influence of Early Season Performance and Team Value on Chinese Football Super League Rankings. Proceedings of 3rd International Conference on Big Data Analysis (ICBDA 2018), IEEE to appear.
DOI:		 EI
87. Fournier-Viger, P., Li, X., Yao, J., Lin, J. C.-W. (2018). Interactive Discovery of Statistically Significant Itemsets. Proc. 31rd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2018), Springer LNAI, pp. 101-113.
DOI: 10.1007/978-3-319-92058-0_10		 EICorr. Author
88. Wu, J. W.M., Lin, J.C-W., Pirouz, M., Fournier-Viger, P. (2018). New Tighter Upper Bounds for Mining High Average-Utility Itemsets. Proc. 2018 International Conference on Big Data and Education (ICBDE 2018), ACM, pp. 27-32.
DOI: 10.1145/3206157.3206168
89. Amirat, H., Benslimane, A., Fournier-Viger, P., Lagraa, N. (2018). LocRec: Rule-based Successive Location Recommendation in LBSN. Proceedings of 2018 IEEE International Conference on Communications (ICC 2018), IEEE, pp. 1-6.
DOI: 10.1109/ICC.2018.8422183		 EICCF-C
90. Djenouri, Y., Lin, J. C.-W., Djenouri, D., Belhadi, A., Fournier-Viger, P. (2018). GBSO-RSS: GPU-based BSO for Rules Space Summarization. Proc. 1st International Conference on Big Data and Deep Learning Applications (ICBDL 2018), 123-129.
DOI: 10.1007/978-981-13-0869-7_14		 EI
91. de Souza, K. V.-C. K., Almhana, C., Fournier-Viger, P., Almhana, J. (2018). Radio Data Transmission Reduction in Power-Constrained WSN. Proceedings of 14th International Wireless Communications & Mobile Computing Conference (IWCMC 2018), IEEE, pp. 285-290.
DOI: 10.1109/IWCMC.2018.8450353		 EI
92. Lin, J. C.-W., Li, Y., Fournier-Viger, P., Tan, L. (2018). Mining high utility itemsets from multiple databases. Proc. 2nd Intern. Conf. on Smart Vehicular Technology, Transportation, Communication and Applications (VTCA 2018), Springer, pp. 139-146.
DOI: 10.1007/978-3-030-04585-2_17		 EI
2017 93. Kiran, U., Venkatesh, J. N., Fournier-Viger, P., Toyoda, M., Reddy, P. K., Kitsuregawa, M. (2017). Discovering Periodic Patterns in Non-Uniform Temporal Databases. Proc. 21st Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD 2017) Part II, Springer, LNAI, pp. 604-617.
DOI: 10.1007/978-3-319-57529-2_47		 EI
Acceptance rate: 28%
94. Gan, W., Lin, J. C.-W., Fournier-Viger, P., Chao, H.-C., Tseng, V.S. (2017). Mining High-Utility Itemsets with both Positive and Negative Unit Profits from Uncertain Databases. Proc. 21st Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD 2017) Part I, Springer, LNAI, pp. 434-446
DOI: 10.1007/978-3-319-57454-7_34		 EI
Acceptance rate: 28%
95. Lin, J. C.-W., Zhang, J., Fournier-Viger, P. (2017). High Utility Sequential Pattern Mining with Multiple Minimum Utility Thresholds. Proc. of the APWeb-WAIM Joint Conference on Web and Big Data 2017(APWeb-WAIM 2017), Springer, pp.215-229.
DOI: 10.1007/978-3-319-63579-8_17		 EICCF-C
Acceptance rate: 18%
96. Gan, W., Lin, J. C.-W., Fournier-Viger, P., Chao, H.-C.(2017). Exploiting High Utility Occupancy Patterns. Proc. of the APWeb-WAIM Joint Conference on Web and Big Data 2017(APWeb-WAIM 2017), Springer, pp.239-247
DOI: 10.1007/978-3-319-63579-8_19		 EICCF-C
Acceptance rate: 18%
97. Gan, W., Lin, J. C.-W., Fournier-Viger, P., Chao, H.-C.(2017). Extracting Non-redundant Correlated High Utility Purchase Behaviors. Proc. 19th Intern. Conf. on Data Warehousing and Knowledge Discovery (DaWaK 2017), Springer, pp. 433-446
DOI: 10.1007/978-3-319-64283-3_32		 EI
98. Djenouri, Y., Belhadi, A., Fournier-Viger, P., Lin, J. C.-W. (2017). An Hybrid Multi-Core/GPU-based Mimetic Algorithm for Big Association Rule Mining. Proc. 11th Intern. Conference on Genetic and Evolutionary Computing (ICGEC 2017), Springer, pp. 56-65
DOI: 10.1007/978-981-10-6487-6_8
99. Zhang, X., Wu, C.-W., Fournier-Viger, P., Van, L.-D., Tseng, Y.-C. (2017). Analyzing Students' Attention in Class Using Wearable Devices. Proc. 18th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2017), IEEE, pp. 1-9.
DOI: 10.1109/WoWMoM.2017.7974306		 EICCF-C
100.

Dalmas, B., Fournier-Viger, P., Norre, S. (2017). TWINCLE: A Constrained Sequential Rule Mining Algorithm for Event Logs. Proc. 9th International KES Conference (IDT-KES 2017), Elsevier, pp. 205-214
DOI: 10.1016/j.procs.2017.08.069
101. Lin, J. C.-W., Ren, S., Fournier-Viger, P., Hong, T.-P. (2017). Mining of High Average-Utility Itemsets with a Tighter Upper-Bound Model. Proceedings of 4th Multidisciplinary International Social Networks Conference on Social Informatics 2017 (MISNC 2017), to appear.
DOI:
102.

Liu, T., Fournier-Viger, P., Hohmann, A. (2017). Using Diagnostic Analysis to Discover Offensive Patterns in a Football Game. Proceedings of International Conference on Data Science and Business Analytics (ICDSBA 2017), Springer, pp. 381-386.
DOI: 10.1007/978-3-319-72745-5_43

103. de Souza, K. V.-C. K., Almhana, C., Fournier-Viger, P., Almhana, J. (2017). Energy-Efficient Partitioning Clustering Algorithm for Wireless Sensor Network. Proceedings of 10th EAI International Wireless Internet Conference (WiCON 2017), Springer, pp. 14-23.
DOI: 10.1007/978-3-319-90802-1_2		 EI
2016 104. Fournier-Viger, P., Lin, C.W., Wu, C.-W., Tseng, V. S., Faghihi, U. (2016). Mining Minimal High-Utility Itemsets. Proc. 27th International Conference on Database and Expert Systems Applications (DEXA 2016). Springer, LNCS, pp. 88-101. [ppt] [source code] [video]
DOI: 10.1007/978-3-319-44403-1_6
 EICCF-CCorr. Author
105. Lin, C.W., Gan, W., Fournier-Viger, P., Chen, H.-C. (2016). More Efficient Algorithms for Mining High-Utility Itemsets with Multiple Minimum Thresholds. Proc. 27th International Conference on Database and Expert Systems Applications (DEXA 2016). Springer, LNCS, pp. 71-87.
DOI: 10.1007/978-3-319-44403-1_5		 EICCF-C
106. Lin, C.W., Gan, W., Fournier-Viger, P., Chen, H.-C. (2016). Mining Recent High-Utility Patterns from Temporal Databases with Time-Sensitive Constraint. Proc. 18th Intern. Conf. on Data Warehousing and Knowledge Discovery (DAWAK 2016). Springer, LNCS, pp. 3-16
DOI: 10.1007/978-3-319-43946-4_1		 EI
107. Fournier-Viger, P., Lin, C.W., Gomariz, A., Soltani, A., Deng, Z., Lam, H. T. (2016). The SPMF Open-Source Data Mining Library Version 2. Proc. 19th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2016) Part III. Springer, pp. 36-40. [software]
DOI: 10.1007/978-3-319-46131-1_8		 EICCF-BCorr. Author
108. Fournier-Viger, P., Zida, S. Lin, C.W., Wu, C.-W., Tseng, V. S. (2016). EFIM-Closed: Fast and Memory Efficient Discovery of Closed High-Utility Itemsets. Proc. 12th Intern. Conference on Machine Learning and Data Mining (MLDM 2016). Springer, LNAI, pp. 199-213. [short ppt][long ppt][source code]
DOI: 10.1007/978-3-319-41920-6_15		 EICorr. Author
109. Lin, C.W., Gan, W., Fournier-Viger, P., Hong, T.-P. (2016). Efficient Mining of Weighted Frequent Itemsets in Uncertain Databases. Proc. 12th Intern. Conference on Machine Learning and Data Mining (MLDM 2016). Springer, LNAI, pp. 236-250.
DOI: 10.1007/978-3-319-41920-6_18		 EI
110. Fournier-Viger, P., Lin, C.W., Duong, Q.-H., Dam, T.-L. (2016). FHM+: Faster High-Utility Itemset Mining using Length Upper-Bound Reduction. Proc. 29th Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2016), Springer LNAI, pp. 115-127. [ppt] [source code]
DOI: 10.1007/978-3-319-42007-3_11		 EICorr. Author
111. Lin, C.W., Li, T., Fournier-Viger, P., Hong, T.-P., Su, J.-W. (2016). Fast Algorithms for Mining Multiple Fuzzy Frequent Itemsets. Proc. 16th IEEE Conference on Fuzzy Systems (FUZZY-IEEE 2016), IEEE, to appear
DOI: 10.3233/IFS-151936
112. Lin, C.W., Ren, S., Fournier-Viger, P., Su, J.-H., Vo, B. (2016). A More Efficient Algorithm to Mine High Average-Utility Itemsets. Proc. 12th Intern. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2016). Springer, pp 101-110.
DOI:
113. Lin, C.-W., Zhang, J., Fournier-Viger, P., Hong, T.-P., Chen, C.-M. (2016). Efficient Mining of Short Periodic High-Utility Itemsets. Proc. 27th IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016). IEEE, to appear
DOI: 10.1109/SMC.2016.7844710		 CCF-C
114. Lin, C.W., Gan, W., Fournier-Viger, P., Han-Chieh, C. (2016). More Efficient Algorithms for Mining Frequent Patterns with Multiple Minimum Supports. Proc. 17th International Conference Web-Age Information Management (WAIM 2016), Springer LNAI, pp. 3-16.
DOI: 10.1007/978-3-319-39937-9_1		 CCF-C
115. Lin, C.W., Gan, W., Fournier-Viger, P., Han-Chieh, C. (2016). Mining Recent High Expected Weighted Itemset from Uncertain Databases. Proc. 18th Asia-Pacific Web Conference (APWeb 2016), Springer, LNCS, pp. 581-593.
DOI: 10.1007/978-3-319-45814-4_47		 CCF-C
116. Lin, C.W., Li, T., Fournier-Viger, P., Hong, T. P., Tseng, V. S. (2016). Efficient Mining of Uncertain Data to Discover High-Utility Itemsets. Proc. 17th International Conference Web-Age Information Management (WAIM 2016), Springer LNAI, pp. 17-30.
DOI: 10.1007/978-3-319-39937-9_2		 CCF-C
117. Voznak, M., Rozhon, J., Mikulec, M., Rezac, F., Komosny, D., Lin, J.C.-W.,Fournier-Viger, P. (2016) Employing the neural networks to parametrically assess the quality of a voice call. Proc. 2016 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2016), pp. 1-5.
DOI: 10.1109/SPECTS.2016.7570521
118. Fournier-Viger, P., Lin, C. W., Dinh, T., Le, H. B. (2016). Mining Correlated High-Utility Itemsets Using the Bond Measure. Proc. 11 th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2016), Springer LNAI, pp.53-65. [ppt] [source code]
DOI: 10.1007/978-3-319-32034-2_5		 Corr. Author
119. Fournier-Viger, P., Lin, C.W., Duong, Q.-H., Dam, T.-L. (2016). PHM: Mining Periodic High-Utility Itemsets. Proc. 16th Industrial Conference on Data Mining. Springer LNAI 9728, pp. 64-79. [ppt] [source code]
DOI: 10.1007/978-3-319-41561-1_6		 Corr. Author* Nominated for best paper award (one of the three finalists)*
120. Lin, C.W., Li, T., Fournier-Viger, P., Hong, T.-P., Su, J.-H. (2016). Efficient Mining of High Average-Utility Itemsets with Multiple Minimum Thresholds. Proc. 16th Industrial Conference on Data Mining. Springer LNAI 9728, pp. 14-28.
DOI: 10.1007/978-3-319-41561-1_2
121. Fournier-Viger, P., Zida, S., Lin, C.W., Wu, C.W., Tseng., V. (2016). Efficient closed high-utility itemset-mining. Proc. 31th Symposium on Applied Computing (ACM SAC 2016). ACM Press, pp. 898-900. [source code]
DOI: 10.1145/2851613.2851884		 * Nominated for best poster award (one of the six finalists)*
122. Lin, C.W., Gan, W., Fournier-Viger, P., Hong, T.-P. (2016). Efficient Algorithms for Mining Recent Weighted Frequent Itemsets in Temporal Transactional Databases. Proc. 31th Symposium on Applied Computing (ACM SAC 2016). ACM Press, pp.861-866
DOI: 10.1145/2851613.2851648
123. Faghihi, U., Chatman, D., Gautier, N., Gholson, J., Gholson, J., Lipkal, M., Dill, R, Fournier-Viger, P., Maldonado-Bouchard, S. (2016). How to Apply Gamification Techniques to Design a Gaming nvironment for Algebra concepts. Proc. 3rd Intern. Conf. on E-Learning, E-Education, and Online Training (eLEOT 2016), Springer LNICST, 8 pages.
DOI: 10.1007/978-3-319-49625-2_8
124. Pokou J. M., Fournier-Viger, P., Moghrabi, C. (2016). Authorship Attribution Using Small Sets of Frequent Part-of-Speech Skip-grams. Proc. 29th Intern. Florida Artificial Intelligence Research Society Conference (FLAIRS 29), AAAI Press, pp. 86-91 [datasets].
DOI:
125. Pokou J. M., Fournier-Viger, P., Moghrabi, C. (2016). Using Frequent Fixed or Variable-Length POS Ngrams or Skip-grams for Blog Authorship Attribution . Proc. 12th Intern. Conf. on Artificial Intelligence Applications and Innovations (AIAI 2016), Springer LNAI, pp. 63-74 [datasets].
DOI: 10.1007/978-3-319-44944-9_6
126.

Lin, J. C.-W., Liu, Q., Fournier-Viger, P., Hong, T.-P., Zhan, J., Voznak, M. (2016). An Efficient Anonymous System for Transaction Data. Proceedings of 3rd Multidisciplinary International Social Networks Conference on Social Informatics 2016 (MISNC 2016), pp. 28.
DOI: 10.1145/2955129.2955136
127. Lin, J. C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P. (2016). Mining Discriminative High Utility Patterns. Proc. 8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016), Springer, pp. 219-229.
DOI: 10.1007/978-3-662-49390-8_21
128. Lin, J. C.-W., Lv, X., Fournier-Viger, P., Wu, T.-Y., Hong, T.-P. (2016). Efficient Mining of Fuzzy Frequent Itemsets with Type-2 Membership Functions. Proc. 8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016), Springer, pp. 191-200.
DOI: 10.1007/978-3-662-49390-8_18
129. Fournier-Viger, P., Lin, C.-W., Duong, Q.-H., Dam, T.-L., Sevcic, L., Uhrin, D., Voznak, M. (2016). PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures. Proc. 2nd Czech-China Scientific Conference 2016, Elsevier, 10 pages. [source code] [video] [ppt]
DOI:

130. Pokou J. M., Fournier-Viger, P., Moghrabi, C. (2016). Authorship Attribution using Variable-Length Part-of-Speech Patterns. Proc. 7th Intern. Conf. on Agents and Artificial Intelligence (ICAART 2016), pp. 354-361 [datasets].
DOI: 10.5220/0005710103540361
2015 131. Zida, S., Fournier-Viger, P., Lin, J. C.-W., Wu, C.-W., Tseng, V.S. (2015). EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining. Proceedings of the 14th Mexican Intern. Conference on Artificial Intelligence (MICAI 2015), Springer LNAI 9413, pp. 530-546. [ppt] [source code]
132. Dougnon, Y. R., Fournier-Viger, P., Lin, J. C.-W., Nkambou, R. (2015). More Accurate User Profile Inference in Online Social Networks. Proceedings of the 14th Mexican Intern. Conference on Artificial Intelligence (MICAI 2015), Springer LNAI 9414, pp. 533-546.
133. Fournier-Viger, P., Lin, J. C.-W., Gueniche, T., Barhate, P. (2015). Efficient Incremental High Utility Itemset Mining. Proc. 5th ASE International Conference on Big Data (BigData 2015), 6 pages. [ppt] [video] [source code]
134. Lin, J. C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P., Tseng, V. S. (2015). Mining Potential High-Utility Itemsets over Uncertain Databases. Proc. 5th ASE International Conference on Big Data (BigData 2015), 6 pages. [video]
135. Gueniche, T., Fournier-Viger, P., Raman, R., Tseng, V. S. (2015). CPT+: Decreasing the time/space complexity of the Compact Prediction Tree. Proc. 19th Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD 2015), Springer, LNAI9078, pp. 625-636.
[ppt] [Source code: Java version (original) and a Cython version (unofficial)]

accept. rate: 6 %
(long presentation)

CCF-C
136. Zida, S., Fournier-Viger, P., Wu, C.-W., Lin, J. C. W., Tseng, V.S., (2015). Efficient Mining of High Utility Sequential Rules. Proc. 11th Intern. Conference on Machine Learning and Data Mining (MLDM 2015). Springer, LNAI 9166, pp. 157-171. [source code] * Nominated for best paper award (one of the three finalists)*
137. Wu, C.W., Fournier-Viger, P., Gu, J.-Y., Tseng, V.S. (2015). Mining Closed+ High Utility Itemsets without Candidate Generation. Proc. 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2015), pp. 187-194. [source code] * Merit Paper Award * 🏆
138. Tseng, V. S., Wu, C.-W., Lin, J.-H., Fournier-Viger, P. (2015). UP-Miner: A Utility Pattern Mining Toolbox. Proc. of IEEE International Conference on Data Mining (ICDM 2015) (demo), pp. 1656-1659. CCF-B
139. Lin, J. C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P., Tseng. V. (2015). Mining High-Utility Itemsets with Various Discount Strategies. Proc. 2015 IEEE/ACM International Conference on Data Science and Advanced Analytics (DSAA’2015), 6 pages, pp. 1-10. accept. rate: 22 %
CCF-C
140. Lin, J. C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P. (2015). Mining Weighted-Frequent Itemsets with Time-Sensitive Constraint. Proc. 17th Asia-Pacific Web Conference (APWeb 2015), Springer, LNCS, pp.635-646. accept. rate: 23 %
CCF-C
141. Dougnon, Y. R., Fournier-Viger, P., Lin, J. C.-W., Nkambou, R. (2015). Accurate Social Network User Profiling. Proc. 38th German Conference on Artificial Intelligence (KI 2015), Springer LNAI 9324, pp. 265-270.
142. Lin, J. C.-W., Yang, L., Fournier-Viger, P., Wu, M.-T., Hong, T.-P., Wang, L. (2015). A Swarm-based Approach to Mine High-Utility Itemsets. Proc. 2nd Multidisciplinary Intern. Social Networks Conference (MISNC 2015). Springer, pp. 572-581.
143
 Lin, J. C.-W., Wu, T.-S., Fournier-Viger, P., Lin, G., Hong, T.-P. (2015). A Sanitization Approach of Privacy Preserving Utility Mining, Proc. 9th Intern. Conference on Genetic and Evolutionary Computing (ICGEC 2015), pp. 47-57. *Best Paper Award* 🏆
144
 Lin, J. C-W., Liu, Q., Fournier-Viger, P., Hong, T.-P., Pan, J. S. (2015). A Swarm-based Sanitization Approach for Hiding Confidential Itemsets. Proc. of the Eleventh Intern. Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 572-583.
145. Lin, J. C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P. (2015). Mining High-Utility Itemsets with Multiple Minimum Utility Thresholds. Proc. 8th Intern. C* Conference on Computer Science & Software Engineering (C3S2E15). ACM, pp. 9-17
146. Dougnon, Y. R., Fournier-Viger, P., Nkambou, R. (2015). Inferring User Profiles in Social Networks using a Partial Social Graph. Proc. 28th Canadian Conference on Artificial Intelligence (AI 2015), Springer, LNAI 9091, pp. 84-99. accept. rate: 18 %
147. Gueniche, T., Fournier-Viger, P. (2015). Réduction de la complexité spatiale et temporelle du Compact Prediction Tree pour la prédiction de séquences. Proc. 15ème conférence Interne sur l'extraction et la gestion des connaissances (EGC 2015), Revue des Nouvelles Technologies de l'Information, vol. E-28, pp.59-70. * Nominated for best paper award *
148. Fournier-Viger, P., Zida, S. (2015). FOSHU: Faster On-Shelf High Utility Itemset Mining– with or without negative unit profit. Proc. 30th Symposium on Applied Computing (ACM SAC 2015). ACM Press, pp. 857-864. [source code] accept. rate: 20 %
2014 149. Fournier-Viger, P., Wu, C.W., Tseng, V.S. (2014). Novel Concise Representations of High Utility Itemsets using Generator Patterns. Proc. 10th Intern. Conference on Advanced Data Mining and Applications (ADMA 2014), Springer LNCS 8933, pp. 30-43. [ppt] [source code] *Best Paper Award* 🏆
150. Fournier-Viger, P. (2014). FHN: Efficient Mining of High-Utility Itemsets with Negative Unit Profits. Proc. 10th Intern. Conference on Advanced Data Mining and Applications (ADMA 2014), Springer LNCS 8933, pp. 16-29. [ppt][source code]
151. Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R. (2014). Fast Vertical Mining of Sequential Patterns Using Co-occurrence Information. Proc. 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2014) Part 1, Springer, LNAI, 8443. pp. 40-52. [ppt][source code] accept. rate: 27 %
CCF-C
*Most Influential Paper Award (at PAKDD 2024)* 🏆
152. Fournier-Viger, P., Gomariz, A., Sebek, M., Hlosta, M. (2014). VGEN: Fast Vertical Mining of Sequential Generator Patterns. Proc. 16th Intern. Conf. on Data Warehousing and Knowledge Discovery (DAWAK 2014), Springer, LNCS 8646, pp. 476--488. [source code] accept. rate: 31 %
153. Fournier-Viger, P., Gueniche, T., Zida, S., Tseng, V. S. (2014). ERMiner: Sequential Rule Mining using Equivalence Classes. Proc. 13th Intern. Symposium on Intelligent Data Analysis (IDA 2014), Springer, LNCS 8819, pp. 108-119. [source code]
154. Mwamikazi, E., Fournier-Viger, P., Moghrabi, C., Baudouin, R. (2014). A Dynamic Questionnaire to Further Reduce Questions in Learning Style Assessment. Proc. 10th Int. Conf. Artificial Intelligence Applications and Innovations (AIAI2014), Springer, LNAI, pp. 224-235.
155. Fournier-Viger, P., Wu, C.-W., Zida, S., Tseng, V. S. (2014) FHM: Faster High-Utility Itemset Mining using Estimated Utility Co-occurrence Pruning. Proc. 21st Intern. Symposium on Methodologies for Intelligent Systems (ISMIS 2014), Springer, LNAI, pp. 83-92. [ppt][source code][video]
156. Gueniche, T., Fournier-Viger, P., Nkambou, R., Tseng, V. S. (2014) WBPL: An Open-Source Library for Predicting Web Surfing Behaviors. Proc. 21st Intern. Symposium on Methodologies for Intelligent Systems (ISMIS 2014), Springer, LNAI, pp. 524-529.
157. Fournier-Viger, P., Wu, C.-W., Gomariz, A., Tseng, V. S. (2014). VMSP: Efficient Vertical Mining of Maximal Sequential Patterns. Proc. 27th Canadian Conference on Artificial Intelligence (AI 2014), Springer, LNAI, pp. 83-94. [ppt] [source code] accept. rate: 23 %
158. Mwamikazi, E., Fournier-Viger, P., Moghrabi, C., Barhoumi, A., Baudouin, R. (2014). An Adaptive Questionnaire for Automatic Identification of Learning Styles. Proc. 27th Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2014), Springer, LNAI 8481, pp. 399-409.
2013 159. Fournier-Viger, P., Gomariz, A., Gueniche, T., Mwamikazi, E., Thomas, R. (2013). TKS: Efficient Mining of Top-K Sequential Patterns. Proc. 9th Intern. Conference on Advanced Data Mining and Applications (ADMA 2013) Part I, Springer LNAI 8346, pp. 109-120. [ppt] [source code] accept. rate: 14 %
( full paper)
160. Gueniche, T., Fournier-Viger, P., Tseng, V. S. (2013). Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction. Proc. 9th Intern. Conference on Advanced Data Mining and Applications (ADMA 2013) Part II, Springer LNAI 8347, pp. 177-188.
[ppt] [Source code: Java version (original) and a Cython version (unofficial)]		 accept. rate: 14 %
(full paper)
161. Fournier-Viger, P., Wu, C.-W., Tseng, V. S. (2013). Mining Maximal Sequential Patterns without Candidate Maintenance. Proc. 9th Intern. Conference on Advanced Data Mining and Applications (ADMA 2013) Part I, Springer LNAI 8346, pp. 169-180. [ppt] [source code]
162. Fournier-Viger, P., Mwamikazi, E., Gueniche, T., Faghihi, U. (2013). Memory Efficient Itemset Tree for Targeted Association Rule Mining. Proc. 9th Intern. Conference on Advanced Data Mining and Applications (ADMA 2013) Part II, Springer LNAI 8347, pp. 95-106. [ppt] [source code]
163. Fournier-Viger, P., Tseng, V. S. (2013). TNS: Mining Top-K Non-Redundant Sequential Rules. Proc. 28th Symposium on Applied Computing (ACM SAC 2013). ACM Press, pp. 164-166. [source code]
164. Snow, E., Moghrabi, C., Fournier-Viger, P. (2013). Assessing Procedural Knowledge in Free-text Answers through a Hybrid Semantic Web Approach. Proc. of the 25th IEEE Intern. Conference on Tools with Artificial Intelligence (ICTAI 2013), IEEE, pp. 698-706. CCF-C
2012 165. Fournier-Viger, P. Gueniche, T., Tseng, V.S. (2012). Using Partially-Ordered Sequential Rules to Generate More Accurate Sequence Prediction. Proc. 8th Intern. Conference on Advanced Data Mining and Applications (ADMA 2012), Springer LNAI 7713, pp. 431-442. accept. rate: 19%
166. Fournier-Viger, P., Nkambou, R., Mayers, A., Mephu Nguifo, E., Faghihi, U. (2012). Multi-Paradigm Generation of Tutoring Feedback in Robotic Arm Manipulation Training. Proceedings of the 11th Intern. Conf. on Intelligent Tutoring Systems (ITS 2012), LNCS 7315, Springer, pp. 233-242. accept. rate: 15 %
167. Fournier-Viger, P., Tseng, V.S. (2012). Mining Top-K Non-Redundant Association Rules. Proc. 20th Intern. Symposium on Methodologies for Intelligent Systems (ISMIS 2012), Springer, LNCS 7661, pp. 31- 40. [source code] * Nominated for best paper award *
168. Fournier-Viger, P., Wu, C.-W., Tseng, V. S. (2012). Mining Top-K Association Rules. Proceedings of the 25th Canadian Conf. on Artificial Intelligence (AI 2012), Springer, LNAI 7310, pp. 61-73. [source code] [ppt] accept. rate: 29 %
169. Fournier-Viger, P., Wu, C.-W., Tseng, V.S., Nkambou, R. (2012). Mining Sequential Rules Common to Several Sequences with the Window Size Constraint. Proceedings of the 25th Canadian Conf. on Artificial Intelligence (AI 2012), Springer, LNAI 7310, pp.299-304. [source code]
2011 170. Wu, C.-W., Fournier-Viger, P., Yu., P. S., Tseng, V. S. (2011). Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets. Proceedings of the 11th IEEE Intern. Conference on Data Mining (ICDM 2011). IEEE CS Press, pp.824-833. accept. rate: 12 %
CCF-B
171. Fournier-Viger, P., Tseng, V. S. (2011). Mining Top-K Sequential Rules. Proceedings of the 7th Intern. Conf. on Advanced Data Mining and Applications (ADMA 2011). LNAI 7121, Springer, pp.180-194. [source code] [ppt] accept. rate: 18 %
( full paper)
172. Fournier-Viger, P., Nkambou, R., Tseng, V. S. (2011). RuleGrowth: Mining Sequential Rules Common to Several Sequences by Pattern-Growth. Proceedings of the 26th Symposium on Applied Computing (ACM SAC 2011). ACM Press, pp. 954-959. [source code][ppt]
173. Fournier-Viger, P., Nkambou, R., Mayers, A., Mephu Nguifo, E., Faghihi, U. (2011). An Hybrid Expertise Model to Support Tutoring Services in Robotic Arm Manipulations. Proceedings of the 10th Mexican Intern. Conference on Artificial Intelligence (MICAI 2011), LNAI 7094, Springer, pp. 478-489.
174. Faghihi, U., Fournier-Viger, P., Nkambou, R. (2011).A Cognitive Tutoring Agent with Episodic and Causal Learning Capabilities.Proceedings of the 15th Intern. Conf. on Artificial Intelligence and Education (AIED 2011). IOS Press, pp. 72-80. accept. rate: 32%
175. Faghihi, U., Fournier-Viger, P., Nkambou, R. (2011). Implementing an Efficient Causal Learning Mechanism in a Cognitive Tutoring Agent. Proceedings of the 24th Intern.Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE 2011), LNAI 6704, Springer, pp.27-36.
176. Faghihi, U., Fournier-Viger, P., Nkambou, R. (2011). A Cognitive Tutoring Agent with Automatic Reasoning Capabilities.Proceedings of the 24th Intern. Florida Artificial Intelligence Research Society Conference (FLAIRS 2011), AAAI press, pp.448-449.
<2010 177. Fournier-Viger, P., Faghihi, U., Nkambou, R., Mephu Nguifo, E. (2010). CMRules: An Efficient Algorithm for Mining Sequential Rules Common to Several Sequences. Proceedings of the 23th Intern. Florida Artificial Intelligence Research Society Conference (FLAIRS 2010). AAAI press, pp. 410-415. [source code] [ppt]
178. Faghihi, U., Fournier-Viger, P., Nkambou, R. & Poirier, P. (2010). The Combination of a Causal Learning and an Emotional Learning Mechanism for an Improved Cognitive Tutoring Agent. Proceedings of the 23th Intern.Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE 2010), LNAI 6097, Springer, pp. 438-449.
179. Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E. & Mayers, A.(2010). ITS in Ill-defined Domains: Toward Hybrid Approaches. Proceedings of the 10th Intern. Conf. on Intelligent Tutoring Systems (ITS 2010), LNCS 6095, Springer, pp. 749-751.
180. Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E. (2009). Exploiting Partial Problem Spaces Learned from Users' Interactions to Provide Key Tutoring Services in Procedural and Ill-Defined Domains. Proc. 14th Intern. Conf. on Artificial Intelligence and Education (AIED 2009). IOS Press. pp. 383-390.
181. Faghihi, U., Fournier-Viger, P., Nkambou, R. & Poirier, P. (2009). A Generic Episodic Learning Model Implemented in a Cognitive Agent by Means of Temporal Pattern Mining. Proc. 22nd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE 2009), LNAI 5579, Springer, pp. 545-555.
182. Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E. & Faghihi, U. (2009). Building Agents that Learn by Observing other Agents Performing a Task - A Sequential Pattern Mining Approach. Proceedings of the 22nd Intern.Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE 2009), SCI 214, Springer, pp. 279-284.
183. Faghihi, U., Fournier-Viger, P., Nkambou, R., Poirier, P., Mayers, A. (2009). How Emotional Mechanism Helps Episodic Learning in a Cognitive Agent. Proceedings of the 2009 IEEE Symposium on Intelligent Agents, pp. 23-30.
184. Faghihi, U., Nkambou, R, Poirier, P., & Fournier-Viger, P. (2009). Emotional Learning and a Combined Centralist-Peripheralist Based Architecture for a More Efficient Cognitive Agent. Proceedings of the 7th IEEE Intern. Conference on Industrial Technology (ICIT 2009), 6 pages.
185. Fournier-Viger, P., Nkambou, R & Mephu Nguifo, E. (2008), A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems. Proceedings of the 7th Mexican Intern. Conference on Artificial Intelligence (MICAI 2008). LNAI 5317, Springer, pp. 765-778. *Best Paper Award* 🏆
 accept. rate: 25%
186. Fournier-Viger, P., Nkambou, R., Mayers, A. (2008). A Framework for Evaluating Semantic Knowledge in Problem-Solving-Based Intelligent Tutoring Systems. Proceedings of the 21th Intern. Florida Artificial Intelligence Research Society Conference (FLAIRS 2008). AAAI press, pp. 409-414.
187. Nkambou, R, Mephu Nguifo, E. & Fournier-Viger, P. (2008). Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain. Proceedings of the 9th Intern. Conference on Intelligent Tutoring Systems (ITS 2008). LNCS 5091, Springer, pp. 395-405. * Nominated for best paper award *
accept. rate: 30%
188. Fournier-Viger, P., Nkambou, R., Mayers, A. (2008). Evaluating Spatial Knowledge through Problem-Solving in Virtual Learning Environments. Proceedings of the Third European Conference on Technology Enhanced Learning (EC-TEL 2008). LNCS 5192, Springer, pp 15-26. *In top 14*
accept. rate: 21%
189. Fournier-Viger, P., Nkambou, R & Mephu Nguifo, E. (2008). A Data Mining Framework for the Acquisition of Procedural Knowledge in Intelligent Tutoring Systems. Proceedings of the 16th Intern. Conference on Computers in Education (ICCE 2008). pp. 153-157.
190. Nkambou, R., Mephu Nguifo, E., Couturier, O. & Fournier-Viger, P. (2007). Problem-Solving Knowledge Mining from Users' Actions in an Intelligent Tutoring System. Proceedings of the 19th Canadian Conference on Artificial Intelligence (AI'2007), LNAI 3501, Springer, pp: 393-404. accept. rate: 17%
191. Fournier-Viger, P., Nkambou, R., Mayers, A., Dubois, D. (2007). Automatic Evaluation of Spatial Representations for Complex Robotic Arms Manipulations. Proceedings of the 7th IEEE Intern. Conference on Advanced Learning Technologies (ICALT 2007), pp: 279-281.
192. Nkambou R., Mephu Nguifo, E., Couturier, O., Fournier-Viger, P. (2007). A Framework for Problem-Solving Knowledge Mining from Users’ Actions. Proceedings of the 13th Intern. Conference on Artificial Intelligence in Education (AIED 2007). pp: 623-625.
193. Fournier-Viger P., Najjar, M., Mayers, A., Nkambou, R. (2006). From Black-box Learning Objects to Glass-Box Learning Objects. Proceedings of the 8th Intern. Conference on Intelligent Tutoring Systems (ITS 2006). LNCS 4053, Springer, pp: 258-267. accept. rate: 32%
194. Fournier-Viger, P., Najjar, M., Mayers, A. (2005). Combining the Learning Objects Paradigm with Cognitive Modelling Theories - a Novel Approach for Knowledge Engineering. Proceedings of the ITI 3rd Intern. Conference on Information & Communication Technology (ICICT 2005). pp: 565-578.

Workshop Papers

2024 1. Nawaz, Z., Nawaz, M. S., Fournier-Viger, P. Selmaoui-Folcher, N.. (2024) Genetic Algorithm for Efficient Descriptive Pattern Mining. Proc. 6th International Workshop on Utility-Driven Mining (UDML 2024), in conjunction with the PAKDD 2024 conference, to appear. Corr. Author
2023 2. Zhao, Z, Chen, X., Junjie, M., Zhang, Q, Fournier-Viger, P., Hawbani, A. (2023) Multi-modal Chinese Fake News Detection. Proc. 1st International Workshop on Multi-Modal Data Analysis (MDA 2023), in conjunction with the ICDM 2023 conference, IEEE ICDM workshop proceedings, to appear.
2022 3. Fournier-Viger, P., Gan, W., Wu, Y., Nouioua, M., Song, W., Truong, T., Van, H. D. (2022). Pattern Mining: Current Challenges and Opportunities. Proceedings of the 1st Workshop on Pattern mining and Machine learning in Big complex Databases (PMDB 2022), held at DASFAA 2022, Springer, 16 pages, to appear. [ppt][video]
DOI: 10.1007/978-3-031-11217-1_3		 Corr. Author
4. Alhusaini, N., Li, J., Fournier-Viger, P., Hawbani, A. (2022) Mining High Utility Itemset with Multiple Minimum Utility Thresholds Based on Utility Deviation. Proc. 5th International Workshop on Utility-Driven Mining (UDML 2022), in conjunction with the ICDM 2022 conference, IEEE ICDM workshop proceedings, to appear.
5. Du, X., He, Y., Fournier-Viger, P., Huang, J. Z. (2022). DenMG: Density-Based Member Generation for Ensemble Clustering. Prof. 15th International Workshop on. Parallel Programming Models and Systems Software for. High-End Computing (P2S2). Held at 51st International Conference on Parallel Processing (ICPP 2022).
2021 6. Nawaz, M. S., Fournier-Viger, P., Chen, G., Nawaz, M. Z., Wu, Y. (2021). Metamorphic Malware Behavior Analysis using Sequential Pattern Mining. Proceedings of the 1st Workshop on Machine Learning in Software Engineering (MLiSE 2021). PKDD 2021 Workshop proceedings, Springer, 15 pages, to appear. [ppt] Corr. Author
7. Fournier-Viger, P., Nawaz, M. S., Song, W., Gan, W. (2021). Machine Learning for Intelligent Industrial Design. Proceedings of the 1st Workshop on Machine Learning in Software Engineering (MLiSE 2021). held at PKDD 2021 Workshop proceedings, Springer, 15 pages, to appear. [ppt] Corr. Author
8. Liu, J., Yang, M., Zhou, M., Feng, S., Fournier-Viger, P. (2021) Enhancing Hyperbolic Graph Embeddings via Contrastive Learning. Proc. NeurIPS 2021 Workshop: Self-Supervised Learning - Theory and Practice. 5 pages. (no archival publication)
9. Nouioua, M., Fournier-Viger, P., Qu, J.-F., Lin, J.-C., Gan, W., Song, W. (2021). CHUQI-Miner: Correlated High Utility Quantitative Itemset Mining. Proc. 4th International Workshop on Utility-Driven Mining (UDML 2021), in conjunction with the ICDM 2021 conference, IEEE ICDM workshop proceedings, to appear. [ppt][video] Corr. Author
10. Chen, Y., Fournier-Viger, P., Nouioua, F., Wu, Y.. (2021). Sequence Prediction using Partially-Ordered Episode Rules. Proc. 4th International Workshop on Utility-Driven Mining (UDML 2021), in conjunction with the ICDM 2021 conference, IEEE ICDM workshop proceedings, to appear.[ppt] [video] Corr. Author
2020 11. Fournier-Viger, P., Ganghuan, H., Zhou, M., Nouioua, M., Liu, J. (2020). Discovering Alarm Correlation Rules for Network Fault Management. Proc. of the International Workshop on Artificial Intelligence for IT Operations (AIOPS), in conjunction with the 18th International Conference on Service-Oriented Computing (ICSOC2020) conference, Springer LNCS series, 12 pages. [Video] Corr. Author
12. Nouioua, M., Wang, Y., Fournier-Viger, P., Lin, J.-C., Wu, J. M.-T. (2020). TKC: Mining Top-K Cross-Level High Utility Itemsets. Proc. 3rd International Workshop on Utility-Driven Mining (UDML 2020), in conjunction with the ICDM 2020 conference, IEEE ICDM workshop proceedings, to appear. [ppt] [video] Corr. Author
2019 13. Lin, J. C.-W., Li, Y., Fournier-Viger, P., Djenouri, Y., Zhang, J. (2019). An Efficient Chain Structure to Mine High-Utility Sequential Patterns. Proc. 2nd International Workshop on Utility-Driven Mining (UDML 2019), in conjunction with the ICDM 2019 conference, IEEE ICDM workshop proceedings, pp. 1013-1019.  
14.

Saideep, C., Kiran, R. U., Zettsu, K., Fournier-Viger, P., Kitsuregawa, M., Reddy, P.K. (2019). Discovering Periodic Patterns in Irregular TimeSeries. Proc. 2nd International Workshop on Utility-Driven Mining (UDML 2019), in conjunction with the ICDM 2019 conference, IEEE ICDM workshop proceedings, pp. 1020-1028.
2018 15. Fournier-Viger, P., Liu, T., Lin, J. C.-W. (2018). Football Pass Prediction using Player Locations. Proc. of the 5th Machine Learning and Data Mining for Sports Analytics (MLSA 2018), in conjunction with the PKDD 2018 conference, Springer LNAI 11330, pp. 1–7, 2019. [source code] [presentation video : HTML5 / AVI ] Corr. Author
16. Li, J., Fournier-Viger, P., Lin, J. C.-W., Truong, T. (2018). Discovering low-cost high utility patterns. 1st International Workshop on Utility-Driven Mining (UDM2018), in conjunction with the KDD 2018 conference, 9 pages. [ppt] Corr. Author
17. Truong, T., Tran, A., Duong, A., Le, B., Fournier-Viger, P.(2018). EHUSM: Mining High Utility Sequences with a Pessimistic Utility Model. 1st International Workshop on Utility-Driven Mining (UDM2018), in conjunction with the KDD 2018 conference (UDML 2018), 9 pages.  
18. Djenouri, Y., Belhadi, A., Fournier-Viger, P., Lin, C.-W. (2018) Discovering Strong Meta Association Rules using Bee Swarm Optimization. 7th Workshop on Biologically Inspired Techniques for Data Mining (BDM'18), in conjunction with the 22nd Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD 2018).
2012 19. Snow, E., Moghrabi, C., Fournier-Viger, P. (2012). Assessing Procedural Knowledge in Open-ended Questions through Semantic Web Ontologies. Proc. of the 8thAustralasian Ontology Workshop (AOW2012), in conjunction with the 25th Australasian Joint Conference on Artificial Intelligence, CEUR, pp. 86-97.  
2008 20. Nkambou, R, Fournier-Viger, P., Dubois, D. (2008). RomanTutor, a Tutoring System for Training Astronauts on Robotic Arm Manipulations. Proceedings of the Demonstration Program of the 9th Intern. Conference on Intelligent Tutoring Systems (ITS 2008), pp. 51-53.
  21. Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E. (2008). A Sequential Pattern Mining Algorithm for Extracting Partial Problem Spaces from Logged User Interactions. Proc. of the 3rd Intern. Workshop on Intelligent Tutoring Systems in Ill-Defined Domain: Assessment and Feedback in Ill-Defined Domains (in conjunction with ITS2008). June 23-27, Montreal, Canada.  

Proceedings

2024. 1. Benavides-Prado, D., Erfani, S. M., Fournier-Viger, P., Boo, Y. L., Koh, Y. S. (2024).: Data Science and Machine Learning - 21st Australasian Conference, AusDM 2023, Auckland, New Zealand, December 11-13, 2023, Proceedings. Communications in Computer and Information Science 1943, Springer 2024, ISBN 978-981-99-8695-8
2022 1. Guerrero, J., Fournier-Viger, P. Proceedings of Asia Conference on Algorithms, Computing and Machine Learning (CACML), IEEE, 2022. DOI: 10.1109/CACML55074.2022.00002 ISBN: 978-1-6654-8290-5
2. P. Fournier-Viger et al.(Eds.): Proceedings of MEDI 2022 workshops, CCIS 1751, pp. x-yy, 2022. 10.1007/978-3-031-23119-3
3. Fournier-Viger, P., Hassan, A., Bellatreche, L. Proceedings of MEDI 2022, LNCS 13761, Springer, 2022. ISBN: 978-3-031-21594-0
4. Fujita, H., Fournier-Viger. P., ... (editors) Proceedings of the 35th Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2022), LNAI 13343, Springer, 2022. ISBN: 978-3-031-08529-1
2021 1. Bhattacharyya, D., Saha, S, K. Fournier-Viger, P. Proceedings of 2nd International Conference on Machine Intelligence and Soft Computing (ICMISC-2021), Advances in Intelligent Systems and Computing, Springer, 2021.
2. Kamp, M. ...  Fournier-Viger, P. ... (editors). Proceedings of PKDD 2021 workshops,Communications in Computer and Information Science, CCIS1524, CCIS1525, Springer, 2021, Volume 1 and Volume 2.
2020 2. Fujita, H., Fournier-Viger. P., Ali, M., Sasaki, J. (editors) Proceedings of the 33rd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2020), Lectures Notes in Artificial Intelligence 12144, Springer, 2020. ISBN: 978-3-030-55788-1.
3. Fujita, H., Fournier-Viger. P., Ali, M., Sasaki, J. (editors) Poster proceedings of the 33rd Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2020), 2020. ISBN: 978-4-901195-48-5
2019 4.

Madria, S., Fournier-Viger, P., Chaudhary, S., Krishna Reddy, P. (editors) Proceedings of the 7th International Conference on Big Data Analytics (BDA 2019), 2019, Lecture Notes in Computer Science 11932, Springer, 2019. ISBN 978-3-030-37187-6
 bda2019 book  iea aie 2020 book   iea aie 2020 book  mlise proceedings
 iea aie 2020 book  iea aie 2020 book   mlise workshop proceedings 2  iea aie 2020 book

French Publications

1. Fournier-Viger, P. (2010), Un modèle hybride pour le support à l'apprentissage dans les domaines procéduraux et mal-définis. Ph.D. Thesis, University of Quebec in Montreal, Montreal, Canada, 184 pages.
2. Fournier-Viger, P., Mephu Nguifo, E., Nkambou, R. (2009), Extraction de motifs séquentiels à partir de traces d'utilisation pour la construction automatique de modèles de tâches dans les systèmes tutoriels intelligents. Short paper. Journée thématique : Fouille de données séquentielles et ses applications, Université d'Orléans, Nov. 27, Orleans, France.
3. Fournier-Viger, P. (2005). Un modèle de représentation des connaissances à trois niveaux de sémantique pour les systèmes tutoriels intelligents. M.Sc. dissertation, University of Sherbrooke, Sherbrooke, Canada, 194 pages. Chapter 4: Une introduction aux logiques de descriptions.

Other contributions

1. Fournier-Viger, P. (2019) Foreword of the Advances in Electrical and Electronic Engineering journal, volume 17, issue 1.
2. Fournier-Viger, P. (2012), Intelligent tutoring systems in Ill-Defined Domains - Bibliography 2003-2012. In Paviotti, G., Rossi, P.G., Zarka, D. (Ed.) Intelligent Tutoring Systems: An overview, Pensa Multimedia, p. 153-161. (book appendix).

Other information

Number of citations per year (Google Scholar – updated 2021/12/31)