SPMFAn Open-Source Data Mining Library

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Citations

SPMF has been cited and/or used in the following publications:

  1. Wani G, Joshi M. Quantitative estimation of time interval of 3-sequences. InReliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), 2016 5th International Conference on 2016 Sep 7 (pp. 441-446). IEEE.
  2. Van Hoan M, Mai LC. (2016) Pattern Discovery in the Financial Time Series Based on Local Trend. InInternational Conference on Advances in Information and Communication Technology 2016 Dec 12 (pp. 442-451). Springer International Publishing.
  3. Dsouza, F. H., & Ananthanarayana, V. S. (2016, March). Document classification with a weighted frequency pattern tree algorithm. In Data Mining and Advanced Computing (SAPIENCE), International Conference on (pp. 29-34). IEEE.
  4. Kadir M, Sobhan S, Islam MZ. (2016) Temporal relation extraction using Apriori algorithm. InInformatics, Electronics and Vision (ICIEV), 2016 5th International Conference on 2016 Dec 1 (pp. 915-920). IEEE.
  5. Schneider J, Locher T. (2016) Obfuscation using Encryption. arXiv preprint arXiv:1612.03345. 2016 Dec 10.
  6. Korczak J, Kaźmierczak A. (2016) Poszukiwanie wzorców analitycznego myślenia menedżera z wykorzystaniem eye trackingu. Przegląd Organizacji. 2016(9):44-9.
  7. Roy A, Ray S, Goswami RT. (2016) Approaches and Challenges of Big Data Analytics—Study of a Beginner. InProceedings of the First International Conference on Intelligent Computing and Communication 2017 (pp. 237-245). Springer Singapore.
  8. Quang MN, Dinh T, Huynh U, Le B. (2016) MHHUSP: An integrated algorithm for mining and Hiding High Utility Sequential Patterns. InKnowledge and Systems Engineering (KSE), 2016 Eighth International Conference on 2016 Dec 1 (pp. 13-18). IEEE.
  9. Khoirroh, Ichmi Rianggi Umu, and Wiwik Suharso. Analisis algorima VMSP pada model sequential pattern dalam data mining. Sesindo (2016).
  10. Dunis, CL, Middelton, PW. (2016) Artificial Intelligence in Financial Markets, Book, Springer.
  11. Saeed AA, Rauf A, Khusro S, Mahfooz S. Compressed Bitmaps Based Frequent Itemsets Mining on Hadoop. InProceedings of the 10th International Conference on Informatics and Systems 2016 May 9 (pp. 159-165). ACM.
  12. Serven Graupera A. Cerca de trajectòries de pacients a través de les etapes d'una malaltia a partir d'històries digitals (Bachelor's thesis, Universitat Politècnica de Catalunya).
  13. García Rudolph A. Supporting the design of sequences of cumulative activities impacting on multiple areas through a data mining approach: application to design of cognitive rehabilitation programs for traumatic brain injury patients.
  14. Wu JM, Zhan J, Lin JC. An ACO-based approach to mine high-utility itemsets. Knowledge-Based Systems. 2016 Nov 12.
  15. Lin JC, Li T, Fournier-Viger P, Hong TP, Su JH. Fast algorithms for mining multiple fuzzy frequent itemsets. InFuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on 2016 Nov 10 (pp. 2113-2119). IEEE.
  16. Cavadenti, Olivier, et al. "What did I do Wrong in my MOBA Game?: Mining Patterns Discriminating Deviant Behaviours." International Conference on Data Science and Advanced Analytics. 2016.
  17. Hsu KW. Efficiently and Effectively Mining Time-Constrained Sequential Patterns of Smartphone Application Usage.
  18. Qin, Zhan, et al. "Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy." Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2016.
  19. Kirchgessner, Martin. Mining and ranking closed itemsets from large-scale transactional datasets. Diss. Université Grenoble Alpes, 2016.
  20. Mansha, Sameen, et al. "Neural Network Based Association Rule Mining from Uncertain Data." International Conference on Neural Information Processing. Springer International Publishing, 2016.
  21. Gad-Elraba, Mohamed, et al. "Technical Report: Exception-enriched Rule Learning from Knowledge Graphs."
  22. Quang MN, Huynh U, Dinh T, Le NH, Le B. An Approach to Decrease Execution Time and Difference for Hiding High Utility Sequential Patterns. InIntegrated Uncertainty in Knowledge Modelling and Decision Making: 5th International Symposium, IUKM 2016, Da Nang, Vietnam, November 30-December 2, 2016, Proceedings 2016 (pp. 435-446). Springer International Publishing.
  23. Ouaro S, Lo M, Malo S, DIOP CT, TRAORE Y. Découverte de motifs fréquents guidée par une ontologie. Revue Africaine de la recherche en informatique et mathématiques appliquées. 2016 Dec 7;25.
  24. Zihayat M, Chan Y, An A. Memory-Adaptive High Utility Sequential Pattern Mining over Data Streams. Machine Learning (ML). 2016.
  25. Karanja SK. Density-based Cluster Analysis Of Fire Hot Spots In Kenya's Wildlife Protected Areas (Doctoral dissertation, University of Nairobi).2016
  26. Castellanos-Paez, Sandra, et al. "Mining useful Macro-actions in Planning." Artificial Intelligence and Pattern Recognition (AIPR), International Conference on. IEEE, 2016.
  27. Titov, Mikhail. Personalization and Data Relation Exploration using Predictive Analytics for the Production and Distributed Analysis System (PanDA). Diss. The University of Texas at Arlington, 2016.
  28. Traore, Boukaye Boubacar, Bernard Kamsu-foguem, and Fana Tangara. "Data mining techniques on satellite images for discovery of risk areas." Expert Systems with Applications (2016).
  29. Shao J, Meng X, Cao L. Mining actionable combined high utility incremental and associated patterns. InAircraft Utility Systems (AUS), IEEE International Conference on 2016 Nov 21 (pp. 1164-1169). IEEE.
  30. Chun-Wei Lin J, Li T, Fournier-Viger P, Hong TP, Voznak M. Efficient mining of high average-utility itemsets. InCommunication, Management and Information Technology: Proceedings of the International Conference on Communication, Management and Information Technology (Iccmit 2016) 2016 Jul 26 (pp. 241-248). CRC Press.
  31. Gad-Elrab, Mohamed H., et al. "Exception-enriched rule learning from knowledge graphs." International Semantic Web Conference. Springer International Publishing, 2016.
  32. Sabitha, M. S., S. Viayalakshmi, and RM Rathikaa Sre. "Big data management system for the harmonization of enterprise model." Computing Technologies and Intelligent Data Engineering (ICCTIDE), International Conference on. IEEE, 2016.
  33. Larrea, Barturén, and José Luis. "Caracterización espacio temporal de la ecofisiología de la" apodanthera biflora" utilizando minería de patrones secuenciales." (2016).
  34. Zihayat M, Wu CW, An A, Tseng VS. Efficiently mining high utility sequential patterns in static and streaming data. Intelligent Data Analysis, Accepted. 2016.
  35. Gad-Elrab, M., Stepanova, D. and Urbani, J., (2016). Exception-enriched rule learning from knowledge graphs. KI 2016: Advances in Artificial Intelligence, p.211.
  36. Lin, Jerry Chun-Wei, Ting Li, Philippe Fournier-Viger, Tzung-Pei Hong, Jimmy Ming-Tai Wu, and Justin Zhan (2016). "Efficient Mining of Multiple Fuzzy Frequent Itemsets." International Journal of Fuzzy Systems (2016): 1-9.
  37. 凃耘昇. 尋求有效率之資料串流頻繁樣式探勘. 淡江大學電機工程學系碩士班學位論文. 2016 Jan 1:1-72.
  38. Laghari, G., Murgia, A. and Demeyer, S., 2016, August. Fine-tuning spectrum based fault localisation with frequent method item sets. InProceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering (pp. 274-285). ACM.
  39. Kardkovács, Zs T., and G. Kovács. "Scalable Mining of Frequent and Significant Sequential Patterns."
  40. Shireesha, R. and Bhutada, S., 2016. A Study of Tools, Techniques, and Trends for Big Data Analytics. IJACTA, 4(1), pp.152-158.
  41. Fan, Cheng. Development of data mining-based big data analysis methodologies for building energy management. Diss. The Hong Kong Polytechnic University, 2016.
  42. Eraslan, S. U. K. R. U., Y. E. L. I. Z. Yesilada, and S. I. M. O. N. Harper. "Scanpath Trend Analysis on Web Pages: Clustering Eye Tracking Scanpaths." ACM Transactions on the Web (Accept subject to minor revisions) (2016).
  43. De Palma, M. Noël, et al. "Fouille et classement d’ensembles fermés dans des données transac-tionnelles de grande échelle."
  44. GATUHA, G. and JIANG, T. (2016), Smart Frequent itemsets mining algorithm based on FP-tree and DIFFset data structures. 2.
  45. Kriegel, Hans-Peter, Erich Schubert, and Arthur Zimek. "The (black) art of runtime evaluation: Are wecomparingalgorithms or implementations?." Knowledge and Information Systems (2016): 1-38.
  46. Zhang, H., Chen, Z., Liu, Z., Zhu, Y. and Wu, C., (2016). Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset. PloS one, 11(8), p.e0160629.
  47. Liu, Z., Wang, Y., Dontcheva, M., Hoffman, M., Walker, S. and Wilson, A., (2016) Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths.
  48. Lin, J.C.W., Wu, T.Y., Fournier-Viger, P., Lin, G., Zhan, J. and Voznak, M., (2016). Fast algorithms for hiding sensitive high-utility itemsets in privacy-preserving utility mining. Engineering Applications of Artificial Intelligence, 55, pp.269-284.
  49. Li, Y., Xu, J., Yuan, Y.H. and Chen, L., 2016. A new closed frequent itemset mining algorithm based on GPU and improved vertical structure.Concurrency and Computation: Practice and Experience.
  50. 王文芳 (2016) Learning Process Analysis Based on Sequential Pattern Mining in a Web-based Inquiry Science Environment.
  51. Fournier-Viger, P., Lin, J.C.W., Wu, C.W., Tseng, V.S. and Faghihi, U., 2016, September. Mining Minimal High-Utility Itemsets. In International Conference on Database and Expert Systems Applications (pp. 88-101). Springer International Publishing.
  52. Rawassizadeh, R., Momeni, E., Dobbins, C., Gharibshah, J. and Pazzani, M.(2016) Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data.
  53. Castellanos-Paez, S., Pellier, D., Fiorino, H. and Pesty, S. (2016). Learning Macro-actions for State-Space Planning. JFPDA 2016
  54. patil, swapnil s., and hridaynath p. khandagale. "enhancing web navigation usability using web usage mining techniques." (2016).
  55. Bhuiyan, M. and Hasan, M.A., 2016. PRIIME: A Generic Framework for Interactive Personalized Interesting Pattern Discovery. arXiv preprint arXiv:1607.05749.
  56. Zhang, W. (2016) Learning From Access Logs to Mitigate Insider Threats. PhD Thesis, Vanderbilt University
  57. Lin, J.C.W., Fournier-Viger, P. and Gan, W., (2016). FHN: An efficient algorithm for mining high-utility itemsets with negative unit profits.Knowledge-Based Systems.
  58. Van Haaren, J., Hannosset, S., & Davis, J. (2016). Strategy discovery in professional soccer match data. In Proceedings of the KDD-16 Workshop on Large-Scale Sports Analytics.
  59. García-Rudolph, A., & Gibert, K. (2016). Understanding effects of cognitive rehabilitation under a knowledge discovery approach. Engineering Applications of Artificial Intelligence, 55, 165-185.
  60. Eraslan, S., Yesilada, Y., & Harper, S. (2016). Trends in Eye Tracking Scanpaths: Segmentation Effect?. In Proceedings of the 27th ACM Conference on Hypertext and Social Media (pp. 15-25). ACM.
  61. Jugo, I., Kovačić, B., & Slavuj, V. (2016, January). Guiding Students towards Frequent High-Utility Paths in an Ill-Defined Domain. In 9th International Conference on Educational Data Mining.
  62. Sozuer, S., Etemoglu, C., & Zeydan, E. (2016, April). A new approach for clustering alarm sequences in mobile operators. In NOMS 2016-2016 IEEE/IFIP Network Operations and Management Symposium (pp. 1055-1060). IEEE.
  63. Somasiri, L.U., Galabada, S.S.G., Wijethunga, H.M., Dayananda, H.M., Nugaliyadde, A., Thelijjagoda, S. and Rajasuriya, M., 2016. D-REHABIA: A Drug Addiction Recovery Through Mobile Based Application.
  64. Chen, H., Chowdhury, O., Li, N., Khern-am-nuai, W., Chari, S., Molloy, I. and Park, Y., 2016, June. Tri-Modularization of Firewall Policies. In Proceedings of the 21st ACM on Symposium on Access Control Models and Technologies (pp. 37-48). ACM.
  65. Wang, K., Sadredini, E., & Skadron, K. (2016, May). Sequential pattern mining with the Micron automata processor. In Proceedings of the ACM International Conference on Computing Frontiers (pp. 135-144). ACM.
  66. Flores Lafosse, N. (2016). Extracción de patrones semánticamente distintos a partir de los datos almacenados en la plataforma Paideia. Thesis. Pontificia Universidad Catolica del peru.
  67. Putelli, L. (2016). Estrazione di regole di associazione da dati RDF. Thesis/report. Politecnico di Milano.
  68. Geetha, P., Ramaraj, E. (2016). Tree Based Space Partition of Trajectory Pattern Mining For Frequent Item Sets. Australian Journal of Basic and Applied Sciences, 10(2), pp. 250-261.
  69. Tax, N., Sidorova, N., Haakma, R., & van der Aalst, W. M. (2016). Mining Local Process Models. arXiv preprint arXiv:1606.0606.
  70. Kerkhoff, R. H. (2016). Interactive Sequence Mining. Master Thesis, Universiteit Utrecht
  71. Молдавская, А. В. (2016). Метод формирования многоуровневых последовательных паттернов. Проблеми програмування, (вип.)), 158-163.
  72. Kim, J., & Hwang, B. (2016). Real-time stream data mining based on CanTree and Gtree. Information Sciences367, 512-528.
  73. Rahman, Anisur, et al. "Finding Anomalies in SCADA Logs Using Rare Sequential Pattern Mining." International Conference on Network and System Security. Springer International Publishing, 2016.
  74. Schweizer, D., Zehnder, M., Wache, H., Witschel, H.F., Zanatta, D. and Rodriguez, M., (2015), December. Using Consumer Behavior Data to Reduce Energy Consumption in Smart Homes: Applying Machine Learning to Save Energy without Lowering Comfort of Inhabitants. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 1123-1129). IEEE.
  75. Wahyuni, E. D., & Djunaidy, A. (2016). Fake Review Detection From a Product Review Using Modified Method of Iterative Computation Framework. In MATEC Web of Conferences (Vol. 58, p. 03003). EDP Sciences.
  76. Sheela YJ, Krishnaveni SH. (2016) A Novel Frequent Pattern Mining Approach with OTSP.I J C T A, 8(5), 2015, pp. 2275-2284
  77. Shah, A., Panchal, K. (2016) Novel Approach to Mine Sequential Frequent Pattern. IJARIIE, pp.381-388.
  78. Garcia‐Martí, I., Zurita‐Milla, R., Swart, A., van den Wijngaard, K. C., van Vliet, A. J., Bennema, S., & Harms, M. (2016). Identifying Environmental and Human Factors Associated With Tick Bites using Volunteered Reports and Frequent Pattern Mining. Transactions in GIS.
  79. Lin, Jerry Chun-Wei, et al. "FDHUP: Fast algorithm for mining discriminative high utility patterns." Knowledge and Information Systems (2016): 1-37.
  80. Duong, Quang-Huy, et al. "An efficient algorithm for mining the top-k high utility itemsets, using novel threshold raising and pruning strategies." Knowledge-Based Systems (2016).
  81. Naulaerts, S., Moens, S., Engelen, K., Berghe, W. V., Goethals, B., Laukens, K., & Meysman, P. (2016). Practical Approaches for Mining Frequent Patterns in Molecular Datasets. Bioinformatics and biology insights10, 37.
  82. Lo, S. L., Cambria, E., Chiong, R., & Cornforth, D. (2016). A multilingual semi-supervised approach in deriving Singlish sentic patterns for polarity detection. Knowledge-Based Systems.
  83. Le, T., & Vo, B. (2016). The lattice‐based approaches for mining association rules: a review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.
  84. Gan, W., Lin, J. C. W., Fournier-Viger, P., & Chao, H. C. (2016). More Efficient Algorithm for Mining Frequent Patterns with Multiple Minimum Supports. In Web-Age Information Management (pp. 3-16). Springer International Publishing.
  85. Traore, Y., Diop, C. T., Malo, S., Lo, M., & Ouaro, S. (2016). Découverte de motifs fréquents guidée par une ontologie.
  86. Amphawan, Komate, et al. "Mining High Utility Itemsets with Regular Occurrence." Journal of ICT Research and Applications 10.2 (2016): 153-176.
  87. Ramadani, J., Wagner, S. (2016). "Are suggestions of coupled file changes interesting?."
  88. der Einreichung, T., (2016) Discovery of Interaction Patterns with Graphical User Interface Usage Mining. Master thesis. Technische universität darmstadt
  89. Jakkam, A., & Busso, C. A Multimodal Analysis Of Synchrony During Dyadic Interaction Using A Metric Based On Sequential Pattern Mining.
  90. Gong, X., 2016. Exploring Human Activity Patterns Across Cities through Social Media Data (Doctoral dissertation, TU Delft, Delft University of Technology).
  91. 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, 10 pages, to appear.
  92. Lin, J.C.W., Li, T., Fournier-Viger, P., Hong, T.P., Zhan, J. and Voznak, M., 2016. An efficient algorithm to mine high average-utility itemsets. Advanced Engineering Informatics, 30(2), pp.233-243.
  93. Ghufron (2016) Applications of Data Mining Association Rule FP-Growth algorithm used to Provide Recommendations in Library Book of Udinus. PSI Udinus 2016
  94. Lin, J. 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. In Industrial Conference on Data Mining (pp. 14-28). Springer International Publishing.
  95. 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.
  96. Zhang, W., 2016. Learning From Access Logs to Mitigate Insider Threats (Doctoral dissertation, Vanderbilt University).
  97. 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.
  98. Suci, A. M. Y. A., & Sitanggang, I. S. (2016). Web-Based Application for Outliers Detection on Hotspot Data Using K-Means Algorithm and Shiny Framework. In IOP Conference Series: Earth and Environmental Science (Vol. 31, No. 1, p. 012003). IOP Publishing.
  99. Das A, Zaniolo C. (2016). Fast Lossless Frequent Itemset Mining in Data Streams using Crucial Patterns.
  100. Zida S, Fournier-Viger P, Lin JC, Wu CW, Tseng VS. EFIM: a fast and memory efficient algorithm for high-utility itemset mining. Knowledge and Information Systems. 2016:1-31.
  101. Fournier-Viger P, Lin JC, Gomariz A, Gueniche T, Soltani A, Deng Z, Lam HT. The SPMF Open-Source Data Mining Library Version 2. InJoint European Conference on Machine Learning and Knowledge Discovery in Databases 2016 Sep 19 (pp. 36-40). Springer International Publishing.
  102. 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, 15 pages, to appear
  103. Lin, J.C.W., Yang, L., Fournier-Viger, P., Wu, J.M.T., Hong, T.P., Wang, L.S.L. and Zhan, J., 2016. Mining high-utility itemsets based on particle swarm optimization. Engineering Applications of Artificial Intelligence55, pp.320-330.
  104. 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, to appear
  105. Hoekstra, J. C. S. (2016). "Predicting train journeys from smart card data: a real-world application of the sequence prediction problem."
  106. Lin, J. C. W., Gan, W., Fournier-Viger, P., Hong, T. P., & Tseng, V. S. (2016). Efficient Algorithms for Mining High-Utility Itemsets in Uncertain Databases. Knowledge-Based Systems.
  107. Gunawan, D. (2016). Evaluasi Performa Pemecahan Database dengan Metode Klasifikasi Pada Data Preprocessing Data mining. Khazanah Informatika2(1).
  108. Mahoto, N., Memon, A. and Teevno, M., (2016). Extraction of Web Navigation Patterns y Means of Sequential Pattern Mining. Sindh University Research Journal-SURJ (Science Series), 48(1).
  109. Bailis, P., Narayanan, D., & Madden, S. (2016). MacroBase: Analytic Monitoring for the Internet of Things. arXiv preprint arXiv:1603.00567.
  110. Fang, Y., Cheng, R., Luo, S., & Hu, J. (2016) On Label-Aware Community Search. Technical report, HKU CS tech report TR-2016-01
  111. Choudhury, S. A. (2016). A Comparative Study of Sequential Pattern Mining Algorithms. Diss. Assam University, Silchar.
  112. Pokou J. M., Fournier-Viger, P., Moghrabi, C. (2016). A Novel Method for Accurate Authorship Attribution. Proc. 7th Intern. Conf. on Agents and Artificial Intelligence (ICAART 2016), 8 pages, to appear.
  113. Aoga, J.O., Guns, T. and Schaus, P., (2016). An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming.
  114. 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, to appear
  115. Kemmar, A., Lebbah, Y., Loudni, S., Boizumault, P. and Charnois, T., (2016). Prefix-projection global constraint and top-k approach for sequential pattern mining. Constraints, pp.1-42.
  116. 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, 14 pages, to appear.
  117. 周朝荣, 徐小琼, 杨柳, & 马小霞. (2016). FP-Tree-Based Approach for Frequent Trajectory Pattern Mining. 电子科技大学学报45(1), 130.
  118. 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 (to appear).
  119. Lin, J. C. W., Gan, W., Fournier-Viger, P., & Hong, T. P. (2016). Efficient Mining of Weighted Frequent Itemsets in Uncertain Databases. In Machine Learning and Data Mining in Pattern Recognition (pp. 236-250). Springer International Publishing.
  120. 姜春茂, 王启明, 申倩, & 许美玉. (2016). A Reliable Storage Model and Transmission Mechanism for the Mobile Cloud Node.电子科技大学学报, 45(1), 114.
  121. Luo, Y.-X., Zou, Y.-Z., Jin, Y., Xie, Bing (2016) A Mailing List Based QA Information Extraction Approach.
  122. Karishma B Hathi , Jatin R Ambasana. (2015) “Top K Sequential Pattern Mining Algorithm.” International Conference on Information Engineering, Management and Security: 115-120.
  123. Neha Dwivedi, Srinivasa Rao Satti (2015) Vertical-format Based Frequent Pattern Mining - A Hybrid Approach, Journal of Intelligent Computing. Vol 6, No. 4, pp. 119-133
  124. Nishant, R. N. (2015). Extracting web navigation patterns using Association Rule Mining. History45(209), 121-126.
  125. Viola, C. A. M. (2015) Engagement E content cycle nei social media. Tesi di laurea Magistrale.
  126. Smoljan, E. (2015). Application of learnable evolution model to optimization problems (Doctoral dissertation, Fakultet elektrotehnike i računarstva, Sveučilište u Zagrebu).
  127. Englin, R. (2015). Indirect association rule mining for crime data analysis. Master Thesis. Eastern Washington University
  128. Feng, X., Zhao, J. and Zhang, Z., (2015). MapReduce-Based H-Mine Algorithm. In 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC) (pp. 1755-1760). IEEE.
  129. Wong, Li-Pei, and Shin Siang Choong. "A Bee Colony Optimization algorithm with Frequent-closed-pattern-based Pruning Strategy for Traveling Salesman Problem." 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2015.
  130. 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.
  131. Bass, S. (2015). Heartbeat location assistance for electrocardiograms. Master thesis.
  132. Dwivedi, N., & Satti, S. R. (2015, October). Set and array based hybrid data structure solution for Frequent Pattern Mining. In Digital Information Management (ICDIM), 2015 Tenth International Conference on (pp. 14-19). IEEE.
  133. Eraslan, S., Yesilada, Y., & Harper, S. Eye Tracking Scanpath Analysis Techniques on Web Pages: A Survey, Evaluation and Comparison .Journal of Eye Movement Research 9(1):2, 1-19
  134. Lněnička, Martin. "AHP Model for the Big Data Analytics Platform Selection." Acta Informatica Pragensia 4.2 (2015): 108-121.
  135. Torres, J., & Abad, C. L. (2015). Análisis comparativo de mecanismos de minería de datos para la generación de reglas de asociación aplicables a caches de Grandes Datos. Revista Tecnológica-ESPOL, 28(5).
  136. Kale, Ms Ashwini A., and S. K. Korde. "A Survey on Uniminer Frame Slog for Data Mining." (2015).
  137. Rathee, S., Kaul, M., Kashyap, A. (2015) R-Apriori: An Efficient Apriori based Algorithm on Spark. Proc. of PIKM'15, ACM Press
  138. Feng X, Zhao J, Zhang Z. MapReduce-Based H-Mine Algorithm[C]//2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC). IEEE, 2015: 1755-1760.
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citation_per_year_of_spmf spmf_data_mining_software_visitor_count

SPMF has been used in a wide range of applications, such as:
  • Web usage mining
  • E-learning
  • Stream mining
  • Library recommendation,
  • Predicting location in social networks
  • restaurant recommendation,
  • Classifying edits on Wikipedia
  • Web page recommendation
  • Insider thread detection on the cloud
  • Linguistics
  • Analyzing DOS attack in network data
  • Anomaly detection in medical treatment
  • Discovery of Antigen patterns
  • Load forecasting
  • Agricultural machinery maintenance
  • Authorship attribution
  • Mnufacturing simulations
  • Retail sale forecasting
  • Mining source code
  • Forecasting crime incidents
  • Analyzing medical pathways
  • Optimizing join indexes in data warehouses
  • Smartphone usage log mining
  • Opinion mining on the web
  • Intelligent and cognitive agents
  • Reducing energy consumption
  • Music Analysis
  • Chemistry
  • Text retrieval
  • Train journey prediction
  • Fault detection in execution traces
  • ….

Please cite SPMF as follows:

Fournier-Viger, P., Lin, C.W., Gomariz, A., Gueniche, T., 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 LNCS 9853,  pp. 36-40.

Copyright © 2008-2017 Philippe Fournier-Viger. All rights reserved.