Philippe Fournier-Viger

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) Sample chapters (drafts); Discovering Periodic High Utility Itemsets in a Discrete Sequence Finding Periodic Patterns in Multiple Discrete Sequences
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); A survey of high utility itemset mining A survey of high utility sequential pattern mining Mining Compact High Utility Itemsets without Candidate Generation Efficient algorithms for high utility itemset mining without candidate generation

Proceedings

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.
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

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. auth.
	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. auth. * --
	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. auth. --
	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. auth.
	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. auth.
	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. auth.
	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. auth.

Journal Papers

			Indexes / SCR rank / ISSN
2021.	1.	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.	ISI, EI, SCI, SCR: Q1 IF: 2.8 ISSN: 1556-4681 CA: Q4 JCR: Q2 CCF-B
	2.	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.	ISI, EI, SCI, SCR: Q1 IF: 2.8 ISSN: 1556-4681 CA: Q4 JCR: Q2 CCF-B
	3.	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.	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C
	4.	Wang, Y., Wu, Y., Li, Y., Yao, F., Fournier-Viger, P., Wu, X. (2021). Self-Adaptive Nonoverlapping Sequential Pattern Mining. Applied Intelligence, to appear.	ISI, EI, SCI, Q1 IF: 3.264 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C
	5.	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]	ISI, EI, SCI, Q1 IF: 3.264 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C, corr. auth.
	6.	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]	ISI, EI, SCI, Q1 IF: 3.264 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C, corr. auth.
	7.	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.	ISI, EI, SCI, Q1 IF: 3.264 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C, corr. auth.
	8.	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.	ISI, EI, SCI, Q1 IF: 3.264 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C, corr. auth.
	9.	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]	ISI, EI, SCI, Q1 IF: 3.264 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C, corr. auth.
	10.	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:	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0020-0255 CA: Q2 JCR: Q1 CCF-B
	11.	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	ISI, EI, SCI, IF: 5.472 ISSN: 1568-4946 CA: Q1, JCR: Q1, corr. auth.
	12.	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)	ISI, EI, SCI, SCR: Q1 IF: 4.6 ISSN: 1046-8188 CA: Q3 JCR: Q1, corr. auth.
	13.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C
	14.	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)	ISI, EI, SCI, _ IF: 2.1 ISSN 0738-4602 CA: Q4 JCR: Q2
	15.	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	ISI, EI, SCI IF: 3.47 ISSN 1386-7857 CA: Q3 JCR: Q3
	16.	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.	ISI, EI, SCI, IF: 5.768 ISSN: 0167-739X CA: Q2 CCF-C
2020.	17.	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	ISI, EI, SCI, Q1 IF: 11.469 ISSN: 2168-2267 CA: Q1 JCR: Q1 CCF-B
	18.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C
	19.	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	ISI, EI, SCI, SCR: Q1 IF: 3.846 ISSN: 1533-5399 CA: Q3 JCR: Q2 CCF-B
	20.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0020-0255 CA: Q2 JCR: Q1 CCF-B
	21.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0020-0255 CA: Q2 JCR: Q1 CCF-B, corr. auth.
	22.	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	ISI, EI, SCI, Q1 IF： ISSN: 1942-4787, corr. auth.
	23.	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	ISI, EI, SCI, Q2 IF: 3.264 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C, corr. auth.
	24.	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	ISI, EI, SCI, Q2 IF: 3.264 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C, corr. auth.
	25.	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	SCI ... IF: 2.394 ISSN: 0368-492X CA: .....
	26.	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	ISI, EI, SCI, SCR: Q2 IF: 4.098 ISSN: 2169-3536 CA: Q2 JCR: Q1
	27.	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	ISI, EI, SCI, SCR: Q2 IF: 4.098 ISSN: 2169-3536 CA: Q2 JCR: Q1, corr. auth.
	28.	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	ISI, EI, SCI, SCR: Q2 IF: 4.098 ISSN: 2169-3536 CA: Q2 JCR: Q1
	29.	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	ISI, EI, SCI, SCR: Q2 IF: 4.098 ISSN: 2169-3536 CA: Q2 JCR: Q1
	30.	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	ISI, EI, SCI, SCR: Q2 IF: 4.098 ISSN: 2169-3536 CA: Q2 JCR: Q1, corr. auth.
	31.	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	ESCI IF: SJR: Q2 ISSN: 1407-6179
	32.	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	ISI, EI, SCI, IF: 0.86 ISSN 1088-467X CA: Q4 JCR: Q4 CCF-C
	33.	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	ISI, EI, SCI, IF: 5.768 ISSN: 0167-739X CA: Q2 CCF-C
	34.	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.	ISSN: 2520-4165 Note: special issue of UDM 2018 workshop papers
	35.	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.	ISSN: 2520-4165, corr. auth. Note: special issue of UDM 2018 workshop papers
2019	36.	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	ISI, EI, SCI SCR: Q1 IF: 7.05 ISSN: 0278-0046 CA: Q1 JCR: Q1
	37.	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	ISI, EI, SCI, SCR: Q1 IF: 4.561 ISSN: 1041-4347 CA: Q2 JCR: Q1 CCF-A
	38.	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.	ISI, EI, SCI, Q1 IF: 4.561 ISSN: 1041-4347 CA: Q2 JCR: Q1 CCF-A
	39.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C, corr. auth.
	40.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C, corr. auth.
	41.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C, corr. auth.
	42.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C
	43.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0020-0255 CA: Q2 JCR: Q1 CCF-B, corr. auth.
	44.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0020-0255 CA: Q2 JCR: Q1 CCF-B, corr. auth.
	45.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0020-0255 CA: Q2 JCR: Q1 CCF-B
	46.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0020-0255 CA: Q2 JCR: Q1 CCF-B
	47.	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.	ISI, EI, SCI SCR: Q1 IF: 1.46 ISSN: 0169-023X CA: Q4 JCR: Q3 CCF-B, corr. auth.
	48.	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.	ISSN 1804-3119 corr. auth.
	49.	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	ISI, EI, SCI, Q1 IF: 4.703 ISSN: 1942-4787 CA: Q3 JCR: Q2
	50.	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	ISI, EI, SCI, Q1 IF: 11.469 ISSN: 2168-2267 CA: Q1 JCR: Q1 CCF-B Web-of-Science Highly-Cited paper
	51.	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	ISI, EI, SCI, SCR: Q1 IF: 2.8 ISSN: 1556-4681 CA: Q4 JCR: Q2 CCF-B
	52.	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	ISI, EI, SCI, SCR: Q1 IF: 2.819 ISSN: 0952-1976 CA: Q2 JCR: Q1 CCF-C
	53.	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	ISI, EI, SCI, SCR: Q2 IF: 4.098 ISSN: 2169-3536 CA: Q2 JCR: Q1
	54.	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	ISI, EI, SCI, Q3 IF: 2.367 ISSN: 1432-7643 CA: Q3 JCR: Q2 CCF-C
	55.	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	ISI, EI, SCI, Q2? IF: 1.910 ISSN: 1868-5137 CA: Q4 JCR: Q3
	56.	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	ISI,EI, SCI IF: 2.396 ISSN: 1562-2479 CA: Q3 JCR: Q2
	57.	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	ISI, EI, SCI, Q2 IF: 2.39 ISSN:0219-1377 CA: Q3 JCR: Q2 CCF-B
	58.	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	ISI, EI, SCI, IF: 5.768 ISSN: 0167-739X CA: Q2 CCF-C
	59.	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: ___	SCI, JCR Q3 IF: 1.230 ISSN: 1547-1063 CA: Q4 JCR: Q3
2018	60.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C
	61.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C
	62.	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	ISI, EI, SCI, Q1 IF: 5.3 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C Web-of-Science Highly-Cited paper
	63.	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	ISI, EI, SCI, Q2 IF: 4.098 ISSN: 2169-3536 CA: Q2 JCR: Q1
	64.	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	ISI, EI, SCI, Q2 IF: 4.098 ISSN: 2169-3536 CA: Q2 JCR: Q1
	65.	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	ISI, EI, SCI, IF: 0.575 ISSN:1367-0751 CA: Q4 JCR: Q3, corr. auth.
	66.	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). to appear.	ISI, EI, SCI, JCR Q3 IF: 0.715 ISSN:1607-9264 CA: Q4 JCR: JCR: Q4
	67.	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	ISI, EI, SCI, Q2 IF: 1.983 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C
	68.	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	ISI, EI, SCI, Q2 IF: 1.983 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C
	69.	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	ISI, EI, SCI, Q2 IF: 1.983 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C
	70.	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(12): 4775-4791 (2018) DOI: 10.1007/s10489-018-1245-8	ISI, EI, SCI, Q2 IF: 1.983 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C
	71.	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	ISI, EI, SCI, IF: 2.845 ISSN: 0888-613X CA: Q3 JCR: Q2 CCF-B
	72.	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	ISI, EI, SCI, Q1 IF: 2.819 ISSN: 0952-1976 CA: Q2 JCR: Q1 CCF-C
	73.	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	ISI, EI, SCI, IF: 4.305 ISSN: 0020-0255 CA: Q2 JCR: Q1 CCF-B
	74.	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	ISI, EI, SCI, Q1 IF: 4.305 ISSN:0020-0255 CA: Q2 JCR: Q1 CCF-B
	75.	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	ISI, EI, SCI, Q1 IF: 4.305 ISSN:0020-0255 CA: Q2 JCR: Q1 CCF-B
	76.	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	ISI, EI, SCI, Q1 IF: 2.819 ISSN: 0952-1976 CA: Q2 JCR: Q1 CCF-C * Best theory award EAAI / IFAC 2020*
	77.	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	ISI, EI, SCI IF: 2.19 ISSN: 1562-2479 CA: Q3 JCR: Q2
	78.	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	ISI, EI, SCI, IF: 0.612 ISSN 1088-467X CA: Q4 JCR: Q4 CCF-C
2017	79.	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	ISI, EI, SCI, Q1 ISSN: 1942-4787 CA: Q3 JCR: Q2
	80.	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.	ISI, EI, SCI, Q1 ISSN: 1942-4787 CA: Q3 JCR: Q2, corr. auth.
	81.	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.	ISSN: 2520-4165, corr. auth.
	82.	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	ISI, EI, SCI, Q2 IF: 1.983 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C
	83.	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	ISI, EI, SCI, Q2 IF: 1.983 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C
	84.	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	ISI, EI, SCI, Q2 IF: 1.983 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C
	85.	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	ISI, EI, SCI, Q2 IF: 1.983 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C, corr. auth.
	86.	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	ISI, EI, SCI, Q2 IF: 4.098 ISSN: 2169-3536 CA: Q2 JCR: Q1
	87.	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	ISI, EI, SCI, Q2 IF: 4.098 ISSN: 2169-3536 CA: Q2 JCR: Q1
	88.	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	ISI, EI, SCI, Q2 IF: 4.098 ISSN: 2169-3536 CA: Q2 JCR: Q1
	89.	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	ISI, EI, SCI, Q3 IF: 2.784 ISSN: 1432-7643 CA: Q3 JCR: Q2 CCF-C
	90.	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	ISI, EI, SCI, Q1 IF: 4.396 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C, corr. auth.
	91.	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	ISI, EI, SCI, Q1 IF: 2.819 ISSN: 0952-1976 CA: Q2 JCR: Q1 CCF-C
	92.	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	ISI, EI, SCI, Q1 IF: 2.819 ISSN: 0952-1976 CA: Q2 JCR: Q1 CCF-C
	93.	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	ISI, EI, SCI, Q2 IF: 2.247 ISSN:0219-1377 CA: Q3 JCR: Q2 CCF-B
	94.	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	ISI, EI, SCI, Q2 IF: 2.247 ISSN:0219-1377 CA: Q3 JCR: Q2 CCF-B
	95.	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	ISI, EI, SCI, Q2 IF: 2.247 ISSN:0219-1377 CA: Q3 JCR: Q2 CCF-B
	96.	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 [powerpoint] [source code] DOI: 10.1007/s10115-016-0986-0	ISI, EI, SCI, Q2 IF: 2.247 ISSN:0219-1377 CA: Q3 JCR: Q2 CCF-B, corr. auth.
	97.	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	ISI, EI, SCI, Q2 IF:_ ISSN: 2169-3536 __________
	98.	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	ISI, EI, SCI, Q2 IF: 1.105 ISSN: 2095-2228 CA: Q4 JCR: Q3 CCF-C
	99.	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	ISI, EI, SCI, Q1 ISSN: 1474-0346 CA: Q2 JCR: Q2 CCF-B
	100.	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.	ISI, EI, SCI, Q4 IF: ISSN: 1796-2021 -----------
	101.	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:	ISI, EI, SCI, JCR Q3 IF: 0.715 ISSN:1607-9264 CA: Q4 JCR: Q4
	102.	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	ISI, EI, SCI, Q1 IF: ISSN: 1932-6203
	103.	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.	ISSN: 2520-4165
2016	104.	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	ISI, EI, SCI, Q1 IF: 2.07 ISSN: 1041-4347 CA: Q2 JCR: Q1 CCF-A
	105.	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	ISI, EI, SCI, Q4 IF: 1.294 ISSN: 0925-9902 CA: Q4 JCR: Q3 CCF-C, corr. auth.
	106.	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	ISI, EI, SCI, Q2 IF: 2.004 ISSN:0219-1377 CA: Q3 JCR: Q2 CCF-B
	107.	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	ISI, EI, SCI, Q3 IF: 2.472 ISSN: 1432-7643 CA: Q3 JCR: Q2 CCF-C
	108.	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	ISI, EI, SCI, Q3 IF: 2.472 ISSN: 1432-7643 CA: Q3 JCR: Q2 CCF-C
	109.	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	ISI, EI, SCI, Q3 IF: 2.472 ISSN: 1432-7643 CA: Q3 JCR: Q2 CCF-C
	110.	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	ISI, EI, SCI Q2 IF: 2.894 ISSN: 0952-1976 CA: Q2 JCR: Q1 CCF-C
	111.	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	ISI, EI, SCI, Q1 IF: 2.68 ISSN: 1474-0346 CA: Q2 JCR: Q2 CCF-B
	112.	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	ISI, EI, SCI, Q2 IF: 1.904 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C
	113.	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	ISI, EI, SCI, Q2 ISSN: 0924-669X CA: Q3 JCR: Q2 CCF-C
	114.	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	ISI, EI, SCI, Q1 IF: 2.68 ISSN: 1474-0346 CA: Q2 JCR: Q2 CCF-B
	115.	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	ISI, EI, SCI, Q1 IF: 4.529 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C, corr. auth.
	116.	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	ISI, EI, SCI, Q1 IF: 4.529 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C
	117.	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	ISI, EI, SCI, Q1 IF: 4.529 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C
	118.	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	ISI, EI, SCI, Q1 IF: 4.529 ISSN: 0950-7051 CA: Q2 JCR: Q1 CCF-C
	119.	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	EI, SCI, Q4 IF: 4.529 ISSN: 0218-1940 CA: Q4 ________ CCF-C
2015	120.	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]	ISI, EI SCI, Q1 IF: 2.07 ISSN: 1041-4347 CCF-A, corr. auth.
	121.	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]	ISI, EI, SCI, Q1 IF: 2.07 ISSN: 1041-4347 CCF-A
	122.	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.	ISI, EI, SCI, Q4 ISSN: 1582-7445 CCF-B
	123.	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.	ISI, EI, SCI, Q4 IF: 1.81 ISSN: 1064-1246
	124.	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.	ISI, EI, SCI, Q2 IF: 2.21 ISSN: 0952-1976 CA: Q2 JCR: Q1 CCF-C
2014	125.	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.	ISI, EI SCI, Q1 IF: 2.47 ISSN: 1532-4435 CCF-A, corr. auth.
2013	126.	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.	ISI, EI, SSCI, Q4 IF: 1.22 ISSN: 1939-1382, corr. auth.
2012	127.	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]	ISI, EI, SCI, Q1 IF: 4.104 ISSN: 0950-7051 CCF-C
2012	128.	Faghihi, U., P. Fournier-Viger, Nkambou, R. (2012). A Computational Model for Causal Learning in Cognitive Agents, Knowledge-Based Systems, Elsevier, 30, 48-56	ISI, EI, SCI, Q1 IF: 4.104 ISSN: 0950-7051 CCF-C
2011	129.	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.	ISI, EI, SCI, Q4 IF: 0.33 ISSN: ISSN: 2169-3536
2011	130.	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.	ISI, EI, SCI, Q1 IF: 2.42 CCF-C
<2010	131.	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:	SSCI IF: 1.34 ISSN: 1436-4522, corr. auth.
	132.	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	ISI, EI, SCI, Q2 IF: 3.14 ISSN: 1541-1672
	133.	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	ISI, EI, SSCI IF: 0.82 ISSN: 1939-1382, corr. auth.
	134.	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	IF: 22 ISSN: 2375-2084, corr. auth.

Conference Papers

2021	1.	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]	* Best paper award* EI, corr. auth.
	2.	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.	EI, corr. auth.
	3.	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
	4.	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	EI, corr. auth.
	5.	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]	EI, corr. auth.
	6.	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.	EI
	7.	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
	8.	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	EI, corr. auth.
	9.	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.	EI CCF-C
	10.	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
	11.	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	EI, corr. auth.
	12.	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
2020	13.	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	EI, corr. auth.
	14.	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
	15.	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] DOI: 10.1007/978-3-030-47436-2_8	EI accept. rate: 21 % CCF-C, corr. auth.
	16.	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	EI, corr. auth.
	17.	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	EI, corr. auth.
	18.	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
	19.	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
	20.	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
	21.	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
	22.	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	23.	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]	EI, corr. auth.
	24.	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	EI, corr. auth.
	25.	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	EI CCF-C
	26.	Ktistakis, R., Fournier-Viger, P., Puglisi, S., 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]	EI accept. rate: 20% CCF-C
	27.	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	EI accept. rate: 24.7 % CCF-C
	28.	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	EI accept. rate: 25%, corr. auth.
	29.	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%
	30.	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	EI accept. rate: 23%, corr. auth.
	31.	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	EI CCF-C
	32.	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	EI * Best paper award* corr. auth.
	33.	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	EI CCF-C
	34.	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	EI CCF-C
	35.	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%
	36.	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%
	37.	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	EI CCF-C
	38.	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
	39.	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
	40.	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
	41.	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
	42.	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	43.	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	EI CCF-C, corr. auth.
	44.	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	EI CCF-B
	45.	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	EI CCF-C
	46.	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	EI Acceptance rate: 17%, corr. auth.
	47.	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%
	48.	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
	49.	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	EI, corr. auth.
	50.	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
	51.	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	EI, corr. auth.
	52.	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
	53.	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	EI CCF-C
	54.	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 ISBN: 978-981-13-0869-7
	55.	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
	56.	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 ISBN: 978-3-030-04585-2
2017	57.	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%
	58.	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%
	59.	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	EI Acceptance rate: 18% CCF-C
	60.	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	EI Acceptance rate: 18% CCF-C
	61.	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
	62.	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
	63.	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	EI CCF-C
	64.	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
	65.	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:
	66.	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	ISBN: 978-3-319-72745-5
	67.	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	68.	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	EI CCF-C, corr. auth.
	69.	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	EI CCF-C
	70.	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
	71.	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	EI CCF-B, corr. auth.
	72.	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. [ppt ] [source code] DOI: 10.1007/978-3-319-41920-6_15	EI, corr. auth.
	73.	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
	74.	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	EI, corr. auth.
	75.	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
	76.	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:
	77.	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
	78.	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
	79.	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
	80.	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
	81.	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
	82.	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. auth.
	83.	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	* Nominated for best paper award (one of the three finalists)* corr. auth.
	84.	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
	85.	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)*
	86.	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
	87.	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
	88.	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:
	89.	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
	90.	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
	91.	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
	92.	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
	93.	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:
	94.	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	95.	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]
	96.	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.
	97.	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]
	98.	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]
	99.	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
	100.	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)*
	101.	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 *
	102.	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
	103.	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
	104.	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
	105.	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.
	106.	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.
	107	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*
	108	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.
	109.	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.
	110.	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 %
	111.	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 *
	112.	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	113.	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*
	114.	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]
	115.	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
	116.	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 %
	117.	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]
	118.	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.
	119.	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]
	120.	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.
	121.	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 %
	122.	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	123.	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)
	124.	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)
	125.	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]
	126.	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]
	127.	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]
	128.	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	129.	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%
	130.	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 %
	131.	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 *
	132.	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 %
	133.	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	134.	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
	135.	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)
	136.	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]
	137.	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.
	138.	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%
	139.	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.
	140.	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	141.	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]
	142.	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.
	143.	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.
	144.	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.
	145.	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.
	146.	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.
	147.	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.
	148.	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.
	149.	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%
	150.	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.
	151.	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%
	152.	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%
	153.	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.
	154.	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%
	155.	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.
	156.	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.
	157.	Najjar, M., Fournier-Viger, P., Lebeau, J.-F. & Mayers., A. (2006). Recalling Recollections according to Temporal Contexts - Applying a Novel Cognitive Knowledge Representation Approach. Proceedings of the 5th IEEE Intern. Conference on Cognitive Informatics (ICCI 2006), pp. 424-433.
	158.	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%
	159.	Najjar, M., Fournier-Viger, P., Mayers, A., Bouchard, F. (2005). Memorising Remembrances in Computational Modelling of Interrupted Activities. Proceedings of the 7th Intern. Conference on Computational Intelligence & Natural Computing (CINC 2005). pp: 483-486.
	160.	Najjar, M., Fournier-Viger, P., Mayers, A., Hallé, J. (2005). DOKGETT - An Authoring Tool for Cognitive Model-based Generation of the Knowledge. Proceedings of the 5th IEEE Intern. Conference on Advanced Learning Technologies (ICALT 2005), pp: 371-375.
	161.	Najjar, M., Fournier-Viger, P., Mayers, A., Hallé, J. (2005). Tools and Structures for Modelling Domain/User Knowledge in Virtual Learning. Proceedings of the 16th AACE World Conference on Educational Multimedia, Hypermedia & Telecommunications (ED-MEDIA 2005). pp: 4023-4028.
	162.	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.
	163.	Najjar, M., Mayers, A., Fournier-Viger, P. (2004). Goal-based Modelling of the Learner Behaviour for Scaffolding Individualised Learning Instructions. Proceedings of the 7th Intern. Conference on Computer and Information Technology (ICCIT 2004). pp: 255-262.
	164.	Najjar, M., Mayers, A., Fournier-Viger, P. (2004). Representing Correct/Erroneous Knowledge during Learning : A Framework for a Novel Cognitive-based Student Model. Proceedings of the 2004 Intern. Conference on Intelligent Knowledge Systems (IKS 2004). pp: 236-242.

Workshop Papers

2021	1.	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.
	2.	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). PKDD 2021 Workshop proceedings, Springer, 15 pages, to appear. [ppt]
	3.
2020	4.	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]
	5.	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]
2019	6.	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.
	7.	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	8.	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 ]
	9.	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, ACM press, 9 pages. [ppt]
	10.	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.
	11.	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	12.	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	13.	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.
	14.	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.

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

Total number of conference, journal, book chapters and workshop papers:
(164 + 136 + 17 + 14) = 335 publications
Number of citations (Google Scholar - 2021/5/5):
7800 citations
My DBLP entry
My Google Scholar page
My ORCID ( 0000-0002-7680-9899)
My ResearchGate page
My Mendeley page
My Arnet Miner page
My Scopus page (author id: 14048484800 )
My Publons page

Number of citations per year (based on G. Scholar, - updated 2020/12/7)

90	51	51	57	72	121	294	403	733	982	1713	1721
<2010	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020

Philippe Fournier-Viger

Distinguished professor, Shenzhen University

Publications

Edited books

Proceedings

Book Chapters

Journal Papers

Conference Papers

Workshop Papers

French Publications

Other contributions

Other information