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

  1. N, T.R., Gupta, R. An efficient feature subset selection approach for machine learning. Multimed Tools Appl (2021).
  2. Nouioua M, Wang Y, Fournier-Viger P, Lin JC, Wu JM. TKC: Mining Top-K Cross-Level High Utility Itemsets. UDML 2020.
  3. Daneshgar FF, Abbaspour M. A two-phase sequential pattern mining framework to detect stealthy P2P botnets. Journal of Information Security and Applications. 2020 Dec 1;55:102645.
  4. Fister Jr I, Fister I. Association rules over time. arXiv preprint arXiv:2010.03834. 2020 Oct 8.
  5. Husák M, Bartoš V, Sokol P, Gajdoš A. Predictive methods in cyber defense: Current experience and research challenges. Future Generation Computer Systems. 2020 Oct 8.
  6. Wu, J., Guo, Z., Wang, Z., Xu, Q. and Wu, Y., Visual analytics of multivariate event sequence data in racquet sports. In 2020 IEEE Conference on Visual Analytics Science and Technology (VAST).
  7. Koga, H. and Noguchi, D., 2020, September. Continuous Similarity Search for Evolving Database. In International Conference on Similarity Search and Applications (pp. 155-167). Springer, Cham.
  8. Bui, H., Vo, B., Nguyen-Hoang, T.A. and Yun, U., 2020. Mining frequent weighted closed itemsets using the WN-list structure and an early pruning strategy. Applied Intelligence, pp.1-21.
  9. Dalleiger S, Vreeken J. The Relaxed Maximum Entropy Distribution and its Application to Pattern Discovery.
  10. Srivastava G, Lin JC, Zhang X, Li Y. Large-Scale High-Utility Sequential Pattern Analytics in Internet of Things. IEEE Internet of Things Journal. 2020 Sep 25.
  11. Fournier-Viger, P., Yang, P., Kiran, R.U., Ventura, S. and Luna, J.M., 2020. Mining Local Periodic Patterns in a Discrete Sequence. Information Sciences.
  12. Kaczko, E. Chancen und Risiken von Learning Analytics in der österreichischen Hochschullehre: eine (wirtschafts-) pädagogische Diskussion.
  13. Kim, Hakkyu, and Dong-Wan Choi. "Recency-based sequential pattern mining in multiple event sequences." Data Mining and Knowledge Discovery (2020): 1-31.
  14. Mahringer, Christian A., Brian T. Pentland, In MS Feldman, B. T. Pentland, L. D’Adderio, K. Dittrich, C. Rerup, and D. Seidl. "Sequence Analysis in Routine Dynamics."
  15. Youssef, Nesma, Hatem Abdulkader, and Amira Abdelwahab. "Evaluating Non-redundant Rules of Various Sequential Rule Mining Algorithms." In International Conference on Advanced Intelligent Systems and Informatics, pp. 429-440. Springer, Cham, 2020.
  16. Dahihande, Janhavi, Akshay Jaiswal, Akshay Anil Pagar, Ajinkya Thakare, Magdalini Eirinaki, and Iraklis Varlamis. "Reducing energy waste in households through real-time recommendations." In Fourteenth ACM Conference on Recommender Systems, pp. 545-550. 2020.
  17. Belise, Kenmogne Edith, and Tayou Djamegni Clementin. "An Efficient Algorithm to Discover Intra-Periodic Frequent Sequences." (2020).
  18. Martínez-Carrascal, J.A. and Valderrama, E., Combining clustering and sequential pattern mining to detect behavioral differences in log data: conceptualization and case study.
  19. Peschel, J., Batko, M. and Zezula, P., 2020, September. Algebra for Complex Analysis of Data. In International Conference on Database and Expert Systems Applications (pp. 177-187). Springer, Cham.
  20. Gomes, H.M., Mining Attribute Evolution Rules in Dynamic Attributed Graphs. In Big Data Analytics and Knowledge Discovery: 22nd International Conference, DaWaK 2020, Bratislava, Slovakia, September 14–17, 2020, Proceedings (p. 167). Springer Nature.
  21. Kong m. Thinking like a computer: an exploratory study of introductory programmers'learning processes in scratch (doctoral dissertation, university of delaware).
  22. Yaghlane, B. B. A SAT-Based Approach for Mining High Utility Itemsets from Transaction Databases. In Big Data Analytics and Knowledge Discovery: 22nd International Conference, DaWaK 2020, Bratislava, Slovakia, September 14–17, 2020, Proceedings (p. 91). Springer Nature.
  23. Boukhetta, S.E., Demko, C., Richard, J. and Bertet, K., 2020. Sequence mining using FCA and the NextPriorityConcept algorithm. In Concept Lattice and Applications (CLA'20).
  24. Kailasam, S., Towards Stable Significant Subgroup Discovery⋆.
  25. Yu, X., Shanmugam, K., Bhattacharjya, D., Gao, T., Subramanian, D. and Xue, L., Hawkesian Graphical Event Models.
  26. Hidouri, Amel, Said Jabbour, Badran Raddaoui, and Boutheina Ben Yaghlane. "A SAT-Based Approach for Mining High Utility Itemsets from Transaction Databases." In International Conference on Big Data Analytics and Knowledge Discovery, pp. 91-106. Springer, Cham, 2020.
  27. Sethi, Krishan Kumar, and Dharavath Ramesh. "High average-utility itemset mining with multiple minimum utility threshold: A generalized approach." Engineering Applications of Artificial Intelligence 96 (2020): 103933.
  28. Sethi, Krishan Kumar, and Dharavath Ramesh. "Correlated High Average-Utility Itemset Mining." In Evolution in Computational Intelligence, pp. 485-497. Springer, Singapore.
  29. Kenmogne, Edith Belise, and Clementin Tayou Djamegni. "An Efficient Algorithm to Discover Intra-Periodic Frequent Sequences." In CARI 2020-Colloque Africain sur la Recherche en Informatique et en Mathématiques Apliquées. 2020.
  30. Fournier-Viger, P., Wang, Y., Lin, J.C.W., Luna, J.M. and Ventura, S., 2020, September. Mining cross-level high utility itemsets. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 858-871). Springer, Cham.
  31. Wu, J.M.T., Teng, Q., Lin, J.C.W., Fournier-Viger, P. and Cheng, C.F., 2020, September. Maintenance of Prelarge High Average-Utility Patterns in Incremental Databases. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 884-895). Springer, Cham.
  32. Song, Wei, Lu Liu, and Chaomin Huang. "TKU-CE: Cross-Entropy Method for Mining Top-K High Utility Itemsets." In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 846-857. Springer, Cham, 2020.
  33. Ahmed, U., Lin, J.C.W., Wu, J.M.T., Djenouri, Y., Srivastava, G. and Mukhiya, S.K., 2020, September. Efficient Mining of Pareto-Front High Expected Utility Patterns. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 872-883). Springer, Cham.
  34. Shakerin, F., 2020. Logic-based Approaches in Explainable AI and Natural Language Understanding (Doctoral dissertation).
  35. Wang, W., 2020. Nonoccurring Sequential Behavior Analytics (Doctoral dissertation).
  36. Traore, Y., Diop, C.T., Kamara-Sangare, F., Malo, S., Lo, M. and Ouaro, S., Frequent patterns for improving categorization in semantic wiki Frequent patterns for improving categorization in semantic wiki.
  37. Hsu, C.L., 2020. A multi-valued and sequential-labeled decision tree method for recommending sequential patterns in cold-start situations. Applied Intelligence, pp.1-21.
  38. Han, X., Liu, X., Li, J. and Gao, H., 2020. Efficient top-k high utility itemset mining on massive data. Information Sciences.
  39. Kini, K. Sampath, and BH Karthik Pai. "Enhanced Processing of Input Data in Clustering Techniques of Data Mining Algorithms." In Advances in Artificial Intelligence and Data Engineering, pp. 497-502. Springer, Singapore.
  40. Martins, A.S., Gromicho, M., Pinto, S., de Carvalho, M. and Madeira, S.C., Learning Prognostic Models using Disease Progression Patterns: Predicting the Need for Non-Invasive Ventilation in Amyotrophic Lateral Sclerosis.
  41. Belhadi A, Djenouri Y, Srivastava G, Djenouri D, Lin JC, Fortino G. Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection. Information Fusion.;65:13-20.
  42. Chen, Z., Liu, Y., Valera-Medina, A., Robinson, F. and Packianather, M., 2020. Multi-faceted modelling for strip breakage in cold rolling using machine learning. International Journal of Production Research, pp.1-14.
  43. Dục, b.G. And nam, v.C.N.V., khai phá mẫu dãy lợI ích cao vớI khoảng cách thờI gian.
  44. Srivastava, Gautam, Jerry Chun-Wei Lin, Alireza Jolfaei, Yuanfa Li, and Youcef Djenouri. "Uncertain-Driven Analytics of Sequence Data in IoCV Environments." IEEE Transactions on Intelligent Transportation Systems (2020).
  45. Belhadi, A., Djenouri, Y., Nørvåg, K., Ramampiaro, H., Masseglia, F. and Lin, J.C.W., 2020. Space–time series clustering: Algorithms, taxonomy, and case study on urban smart cities. Engineering Applications of Artificial Intelligence95, p.103857.
  46. Sohrabi, M.K., 2020. An efficient projection-based method for high utility itemset mining using a novel pruning approach on the utility matrix. Knowledge and Information Systems, pp.1-27.
  47. Peschel, J., Batko, M. and Zezula, P., 2020, July. Techniques for Complex Analysis of Contemporary Data. In Proceedings of the 2020 International Conference on Pattern Recognition and Intelligent Systems (pp. 1-5).
  48. Amaral, T., Mineração de Regras de Exceção em Séries Temporais Multivariadas (Doctoral dissertation, Universidade de São Paulo).
  49. Tkáčik, K., Pattern Mining in Command Histories from Cybersecurity Training.
  50. Zhang, M., Xu, T., Li, Z., Han, X. and Dong, X., 2020. e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules. Symmetry12(8), p.1211.
  51. Lim, Jiyoun. "Technology trend on sequential pattern mining of user behavior data." Review of Korea Contents Association 18, no. 1 (2020): 12-17.
  52. Nowak, J., Korytkowski, M., & Scherer, R. (2020, July). Discovering Sequential Patterns by Neural Networks. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-6). IEEE.
  53. Lin, J.C.W., Wu, J.M.T., Djenouri, Y., Srivastava, G. and Hong, T.P., 2020, July. Mining Multiple Fuzzy Frequent Patterns with Compressed List Structures. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE.
  54. Avila Coaguila, C.E., 2020. Uso de herramientas Open Source en desarrollo web: una revisión de la literatura científica en los últimos 10 años.
  55. Choong, Shin Siang, Li-Pei Wong, Malcolm Yoke Hean Low, and Chin Soon Chong. "A bee colony optimisation algorithm with a sequential-pattern-mining-based pruning strategy for the travelling salesman problem." International Journal of Bio-Inspired Computation 15, no. 4 (2020): 239-253.
  56. Gote, Christoph, Giona Casiraghi, Frank Schweitzer, and Ingo Scholtes. "Predicting Sequences of Traversed Nodes in Graphs using Network Models with Multiple Higher Orders." arXiv preprint arXiv:2007.06662 (2020).
  57. Salvadori, Ivan, Alexis Huf, and Frank Siqueira. "Data Linking as a Service: An Infrastructure for Generating and Publishing Linked Data on the Web." In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 262-271. IEEE, 2020.
  58. Pazhaniraja, N., Sountharrajan, S. and Kumar, B.S., 2020. High utility itemset mining: a Boolean operators-based modified grey wolf optimization algorithm. Soft Computing, pp.1-14.
  59. Darrab, S., Broneske, D. and Saake, G., 2020, July. RPP Algorithm: A Method for Discovering Interesting Rare Itemsets. In International Conference on Data Mining and Big Data (pp. 14-25). Springer, Singapore.
  60. Lessanibahria, S., Fernándezb, C.G. and Gastaldia, L., A Pruning Algorithm for Mining Long and Maximal Length Frequent Itemsets.
  61. Sweetlin, d.J. And sampath, r.S., a survey on utility mining.
  62. Martin, T., Francoeur, G. and Valtchev, P., 2020, August. CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1810-1818).
  63. Pal, A. and Kumar, M., 2020. Distributed synthesized association mining for big transactional data. Sādhanā45(1), pp.1-13.
  64. Lin, Jerry Chun-Wei, Matin Pirouz, Youcef Djenouri, Chien-Fu Cheng, and Usman Ahmed. "Incrementally updating the high average-utility patterns with pre-large concept." Applied Intelligence 50, no. 11 (2020): 3788-3807.
  65. Nguyen, D., Luo, W., Vo, B. and Pedrycz, W., 2020. Succinct contrast sets via false positive controlling with an application in clinical process redesign. Expert Systems with Applications161, p.113670.
  66. Amira, A., Derhab, A., Karbab, E.B., Nouali, O. and Khan, F.A., 2020. TriDroid: a triage and classification framework for fast detection of mobile threats in android markets. Journal of Ambient Intelligence and Humanized Computing, pp.1-25.
  67. Cruz, L.A., Zeitouni, K., da Silva, T.L.C., de Macedo, J.A.F. and da Silva, J.S., 2020. Location prediction: a deep spatiotemporal learning from external sensors data. Distributed and Parallel Databases, pp.1-22.
  68. Dermy O, Brun A. Can We Take Advantage of Time-Interval Pattern Mining to Model Students Activity?. International Educational Data Mining Society. 2020 Jul.
  69. Ahmed, U., Lin, J.C.W., Srivastava, G., Yasin, R. and Djenouri, Y., 2020. An Evolutionary Model to Mine High Expected Utility Patterns From Uncertain Databases. IEEE Transactions on Emerging Topics in Computational Intelligence.
  70. Li, J., Fu, Y., Liu, D. and Xu, R., 2020, June. Improving Fake Product Detection with Aspect-Based Sentiment Analysis. In International Conference on Cognitive Computing (pp. 39-49). Springer, Cham.
  71. Impedovo, A., Loglisci, C., Ceci, M. and Malerba, D., jKarma: a Highly-Modular Framework for Pattern-Based Change Detection on Evolving Data (Discussion Paper).
  72. Marcinowski, M. and Ławrynowicz, A., 2020, June. Predicting the Outbreak of Conflict in Online Discussions Using Emotion-Based Features. In International Conference on Web Engineering (pp. 505-511). Springer, Cham.
  73. Chatterjee, K., Chmelík, M., Karkhanis, D., Novotný, P. and Royer, A., 2020, June. Multiple-Environment Markov Decision Processes: Efficient Analysis and Applications. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 30, pp. 48-56).
  74. Chaudhary, P., Mondal, A. and Reddy, P.K., An improved scheme for determining top-revenue itemsets for placement in retail businesses.
  75. Gan, W., Lin, J.C.W., Zhang, J. and Yu, P.S., 2020. Utility Mining across Multi-Sequences with Individualized Thresholds. ACM Transactions on Data Science1(2), pp.1-29.
  76. Dkhil, S.A., Bennani, M.T., Tekaya, M. and Sethom, H.B.A., 2020, June. Sequence Mining and Property Verification for Fault-Localization in Simulink Models. In International Conference on Dependability and Complex Systems (pp. 1-10). Springer, Cham.
  77. Tulabandhula, T., Vaya, S. and Dhar, A., 2020. Privacy preserving targeted advertising and recommendations. Journal of Business Analytics, pp.1-24.
  78. Goethals, B., A Framework for Pattern Mining and Anomaly Detection in Multi-dimensional Time Series and Event Logs. In New Frontiers in Mining Complex Patterns: 8th International Workshop, NFMCP 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers (p. 3). Springer Nature.
  79. Santoro, D., Tonon, A., & Vandin, F. (2020). Mining Sequential Patterns with VC-Dimension and Rademacher Complexity. Algorithms13(5), 123.
  80. Hsiao, H. S., Tsai, F. H., & Hsu, I. Y. (2020). Development and Evaluation of a Computer Detective Game for Microbial Food Safety Education. Journal of Educational Computing Research, 0735633120924924.
  81. Belhadi A, Djenouri Y, Djenouri D, Lin Jc. A Recurrent Neural Network For Urban Long-term Traffic Flow Forecasting. Applied Intelligence. 2020 May 16.
  82. Jamshed, A., Mallick, B. And Kumar, P., 2020. Deep Learning-based Sequential Pattern Mining For Progressive Database. Soft Computing.
  83. Chen, C. M., & Wang, W. F. (2020). Mining Effective Learning Behaviors In A Web-based Inquiry Science Environment. Journal Of Science Education And Technology.
  84. Vo B, Nguyen LV, Vu VV, Lam MT, Duong TT, Manh LT, Nguyen TT, Nguyen LT, Hong TP. Mining Correlated High Utility Itemsets in One Phase. IEEE Access. 2020 May 12;8:90465-77.
  85. Rizvee, R.A., Arefin, M.F. and Ahmed, C.F., 2020, May. Tree-Miner: Mining Sequential Patterns from SP-Tree. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 44-56). Springer, Cham.
  86. Husák, Martin, Tomáš Bajtoš, Jaroslav Kašpar, Elias Bou-Harb, and Pavel Čeleda. "Predictive cyber situational awareness and personalized blacklisting: A sequential rule mining approach." ACM Transactions on Management Information Systems (TMIS) 11, no. 4 (2020): 1-16.
  87. Wang, Jinyan, Shijian Fang, Chen Liu, Jiawen Qin, Xianxian Li, and Zhenkui Shi. "Top-k closed co-occurrence patterns mining with differential privacy over multiple streams." Future Generation Computer Systems (2020).
  88. Kemmar, A., Lebbah, Y., Loudni, S., Boizumault, P. and Charnois, T., A global constraint for sequential pattern mining A global constraint for sequential pattern mining.
  89. Feremans L. Mining Cohesive Patterns in Sequences and Extreme Multi-label Classification.
  90. Cevallos-macías, J., Solórzano-cadena, R., Palma-menéndez, S. And Verduga-urdánigo, F., 2020. Aplicación De Reglas De Asociación Sobre Microservicios En Las Microempresas. Revista Científica Multidisciplinaria Arbitrada" Yachasun"-issn: 2697-3456, 4(6 Ed. Esp.), Pp.54-72.
  91. Srivastava, G., Lin, J.C.W., Pirouz, M., Li, Y. and Yun, U., 2020. A Pre-large Weighted-Fusion System of Sensed High-Utility Patterns. IEEE Sensors Journal.
  92. Fahed, L., Lenca, P., Haralambous, Y. and Lefort, R., 2020. Distant Event Prediction Based on Sequential Rules. Data Science and Pattern Recognition4(1), pp.1-23.
  93. Matos, J., Pires, S., Aidos, H., Gromicho, M., Pinto, S., de Carvalho, M. and Madeira, S.C., 2020, May. Unravelling Disease Presentation Patterns in ALS Using Biclustering for Discriminative Meta-Features Discovery. In International Work-Conference on Bioinformatics and Biomedical Engineering (pp. 517-528). Springer, Cham.
  94. Cleland, ZacharyW. "Towards a Better Understanding of Human Caused Wildfire in Colorado with Spatial Data Mining." (2020).
  95. Segura‐Delgado, A., Gacto, M.J., Alcalá, R. and Alcalá‐Fdez, J., 2020. Temporal association rule mining: An overview considering the time variable as an integral or implied component. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10(4), p.e1367.
  96. Divakaran, Dinil Mon, Rhishi Pratap Singh, Kalupahana Liyanage Kushan Sudheera, Mohan Gurusamy, and Vinay Sachidananda. "ADROIT: Detecting Spatio-Temporal Correlated Attack-Stages in IoT Networks." Statistics 1, no. 11 (2020): 12.
  97. Hosseininasab, A., 2020. Interpretable Learning and Pattern Mining: Scalable Algorithms and Data-Driven Applications (Doctoral dissertation, Tepper School of Business).
  98. Anguita-Ruiz, Augusto, Alberto Segura-Delgado, Rafael Alcalá, Concepción M. Aguilera, and Jesús Alcalá-Fdez. "eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research." PLOS Computational Biology 16, no. 4 (2020): e1007792.
  99. Raj, S., Ramesh, D., Sreenu, M. And Sethi, K.k., 2020. Eafim: Efficient Apriori-based Frequent Itemset Mining Algorithm On Spark For Big Transactional Data. Knowledge And Information Systems.
  100. Li, Zhiyang, Fengjuan Chen, Junfeng Wu, Zhaobin Liu, and Weijiang Liu. "Efficient weighted probabilistic frequent itemset mining in uncertain databases." Expert Systems (2020): e12551.
  101. Islam, M.R. and Zibran, M.F., 2020, March. How bugs are fixed: exposing bug-fix patterns with edits and nesting levels. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 1523-1531).
  102. Jaysawal, B.P. and Huang, J.W., 2020, March. SOHUPDS: a single-pass one-phase algorithm for mining high utility patterns over a data stream. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 490-497).
  103. Ananthi, R.S.M. and Peter, V.J., 2020. Analysis of Algorithms for High Average Utility Itemsets Mining. Studies in Indian Place Names40(71), pp.853-859.
  104. Sethi, K.K. and Ramesh, D., 2020. A fast high average-utility itemset mining with efficient tighter upper bounds and novel list structure. The Journal of Supercomputing, pp.1-31.
  105. Telikani A, Gandomi AH, Shahbahrami A. A survey of evolutionary computation for association rule mining. Information Sciences. 2020 Mar 12.
  106. Tulabandhula, Theja, and Deeksha Sinha. "Optimizing Revenue while showing Relevant Assortments at Scale." arXiv preprint arXiv:2003.04736 (2020).
  107. Fournier-Viger, P., Yang, Y., Yang, P., Lin, J.C.W. and Yun, U., 2020, September. TKE: Mining Top-K Frequent Episodes. In Proceedings of the 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Kitakyushu, Japan (pp. 22-25).
  108. Gan, W., Lin, J.C.W., Zhang, J., Fournier-Viger, P., Chao, H.C. and Philip, S.Y., 2020. Fast utility mining on sequence data. IEEE Transactions on Cybernetics.
  109. Vo, Bay, Loan TT Nguyen, Nguyen Bui, Trinh DD Nguyen, Van-Nam Huynh, and Tzung-Pei Hong. "An efficient method for mining closed potential high-utility itemsets." IEEE Access 8 (2020): 31813-31822.
  110. Chen, H.C., Putra, K.T., Tseng, S.S., Chen, C.L. and Lin, J.C.W., 2020. A spatiotemporal data compression approach with low transmission cost and high data fidelity for an air quality monitoring system. Future Generation Computer Systems108, pp.488-500.
  111. Chen, Hsing-Chung, Karisma Trinanda Putra, Shian-Shyong Tseng, Chin-Ling Chen, and Jerry Chun-Wei Lin. "A spatiotemporal data compression approach with low transmission cost and high data fidelity for an air quality monitoring system." Future Generation Computer Systems 108 (2020): 488-500.
  112. Belhadi, Asma, Youcef Djenouri, Jerry Chun-Wei Lin, and Alberto Cano. "A general-purpose distributed pattern mining system." Applied Intelligence (2020): 1-16.
  113. Kašpar J. Experimenting with the AIDA framework.
  114. Yasir, M., Habib, M.A., Ashraf, M., Sarwar, S., Chaudhry, M.U., Shahwani, H., Ahmad, M. and Faisal, C.M.N., 2020. D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data. IEEE Access8, pp.27375-27392.
  115. Esteves, S., Silva, J.N. and Veiga, L., 2020. Palpatine: Mining Frequent Sequences for Data Prefetching in NoSQL Distributed Key-Value Stores. arXiv preprint arXiv:2002.00215.
  116. Huynh, Huy M., Loan TT Nguyen, Bay Vo, Unil Yun, Zuzana Komínková Oplatková, and Tzung-Pei Hong. "Efficient algorithms for mining clickstream patterns using pseudo-IDLists." Future Generation Computer Systems 107 (2020): 18-30.
  117. Lango, M., Žabokrtský, Z., & Ševčíková, M. (2020). Semi-automatic construction of word-formation networks. Language Resources and Evaluation, 1-30.
  118. Sadredini, E., Rahimi, R., Lenjani, M., Stan, M. and Skadron, K., 2020, March. FlexAmata: A universal and efficient adaption of applications to spatial automata processing accelerators. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems (pp. 219-234).
  119. Lu, Y., Richter, F. and Seidl, T., 2020. Efficient Infrequent Pattern Mining Using Negative Itemset Tree. In Complex Pattern Mining (pp. 1-16). Springer, Cham.
  120. Nguyen, Loan TT, Dinh-Bao Vu, Trinh DD Nguyen, and Bay Vo. "Mining Maximal High Utility Itemsets on Dynamic Profit Databases." Cybernetics and Systems 51, no. 2 (2020): 140-160.
  121. Belhadi, A., Djenouri, Y., Lin, J.C.W., Zhang, C. and Cano, A., 2020. Exploring pattern mining algorithms for hashtag retrieval problem. IEEE Access8, pp.10569-10583.
  122. Hossain, Md Sabir, and Mohammad Shamsul Arefin. "An intelligent system to generate possible job list for freelancers." In Advances in Computing and Intelligent Systems, pp. 311-325. Springer, Singapore, 2020.
  123. Pelletier, Dominique, Nazha Selmaoui‐Folcher, Thomas Bockel, and Thomas Schohn. "A regionally scalable habitat typology for assessing benthic habitats and fish communities: Application to New Caledonia reefs and lagoons." Ecology and Evolution (2020).
  124. Wu, J.M.T., Teng, Q., Lin, J.C.W., Yun, U. and Chen, H.C., 2020. Updating high average-utility itemsets with pre-large concept. Journal of Intelligent & Fuzzy Systems, (Preprint), pp.1-10.
  125. Wu, Tsu-Yang, Jerry Chun-Wei Lin, Unil Yun, Chun-Hao Chen, Gautam Srivastava, and Xianbiao Lv. "An efficient algorithm for fuzzy frequent itemset mining." Journal of Intelligent & Fuzzy Systems Preprint (2020): 1-11.
  126. Merugula, Suneetha, and M. V. P. Rao. "An integrated approach for mining closed and generator high utility itemsets." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 1 (2020): 27-35.
  127. Riguet, M. and Boukhaled, M.A., 2020. La correspondance de motifs, un outil pour l’analyse du discours?. Humanités numériques, (1).
  128. Mai, T., Nguyen, L.T., Vo, B., Yun, U. and Hong, T.P., 2020. Efficient algorithm for mining non-redundant high-utility association rules. Sensors20(4), p.1078.
  129. Chí, Trương Tín, Trần Ngọc Anh, and Dương Văn Hải. "FGenHUSM: Một thuật toán hiệu quả khai thác các chuỗi sinh phổ biến lợi ích cao." Chuyên san Các công trình Nghiên cứu và Phát triển về Công nghệ thông tin và Truyền thông (2019): 57-69.
  130. Gan, W., Lin, J.C.W., Chao, H.C., Fournier-Viger, P., Wang, X. and Yu, P.S., 2020. Utility-Driven Mining of Trend Information for Intelligent System. ACM Transactions on Management Information Systems (TMIS)11(3), pp.1-28.
  131. Gan, W., Lin, J.C.W., Chao, H.C. and Yu, P.S., 2019. Discovering High Utility Episodes in Sequences. arXiv preprint arXiv:1912.11670.
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  948. Sekeres, P. (2013). Learning of Temporal Sequences of Behaviour for Artificial Creature. B.Sc. Thesis. Cztech Technical University in Prague. 50 pages.
  949. Eraslan, S., Yesilada, Y., Harper, S. (2013) eMINE Scanpath Analysis Algorithm. Technical Report, Middle East Technical University. 59 pages.
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  951. Galgani, F. (2013). Knowledge Acquisition with Multiple Summarization Techniques for Legal Text Computer Science & Engineering, Faculty of Engineering, UNSW, Ph.D Thesis.
  952. Abrishami. S., Hasanzadeh, F., Naghibzadeh, M., Jalali, M. (2012). Web Page Recommendation Via Semantic Information and Web Usage Mining. The sixth Iran Data Mining Conference (IDMC 2102).
  953. Kounev, V. (2012). Where will I go next?: Predicting future categorical check-ins in Location Based Social Networks. Proc. 8th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom2012), pp.605-610.
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  955. Abrishami, S., Naghibzadeh, M., Jalali, M. (2012). Web Page Recommendation Based on Semantic Web Usage Mining. Proceedings of the 4th Intern. Conference SocInfo 2012, 393-405.
  956. Tseng, W. R. (2012) Mining Application Usage Patterns of Smartphone Users (智慧型手機使用模�之探勘). M.Sc. Thesis, National Chengchi University, 76 pages.
  957. Bogon, T., Timm, I. J., Lattner, A. D., Paraskevopoulos, D., Jessen, U., Schmitz, M., Wenzel, S., Spieckermann, S. (2012). Towards Assisted Input and Output Data Analysis in Manufacturing Simulation: The EDASIM Approach. Proceedings of the 2012 Winter Simulation Conference. IEEE.
  958. Sultana, R., Vani, Deepti, M., Bhaskhar, P.V., Satish, P., Sekhar, K. KVP. (2012). A Process to Comprehend Different Patterns of Data Mining Techniques for Selected Domains. International Journal of Computer Science Engineering and Technology. 2(9), 1402-1405.
  959. Murlidhar, V., Menezes, B., Sathe, M., & Murlidhar, G. (2012). A clustering based forecast engine for retail sales. Journal of Digital Information Management, 10(4), 219-229.
  960. Fournier-Viger, P. Gueniche, T., Tseng, V.S. (2012). Using Partially-Ordered Sequential Rules to Generate More Accurate Sequence Prediction. Proc. 8th International Conference on Advanced Data Mining and Applications (ADMA 2012), pp. 431-442.
  961. 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.
  962. Lan, X., Witt, H., Katsumura, K., Wang, Q., Bresnick, E. H., Farnham, P. J., Jin, V. X. (2012). Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages. Nucleic Acids Research, pp. 1-15.
  963. Alatrista-Salas, H., Azé, J., Bringay, S., Flouvat, F., Selmaoui-Folcher, N., Cernesson, F., Teisseire, M. (2012). Finding Relevant Sequences With The Least Temporal Contradiction Measure: Application to Hydrological Data. Proceedings of the AGILE'2012 International Conference on Geographic Information Sciencepp. 197-202.
  964. Ben Zakour, A., Maabout, S., Mosbah, M., Sistiaga, M. (2012). Uncertainty interval temporal sequences extraction. Proc. 6th Int. Conf. on Information Systems, Technology and Management (ICISTM), Springer, pp. 259-270.
  965. Mondal, K. C., Pasquier, N., Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S. (2012). A New Approach for Association Rule Mining and Bi-clustering Using Formal Concept Analysis. Proceedings of 8th International Conference MLDM 2012, pp. 86-101.
  966. Zhang, L. Li, Z. Chen, Q., Li, X., Li, N. Lou, Y. (2012). Mining Frequent Association Tag Sequences for Clustering XML Documents. Proc. of the 14th Asia-PacificWeb Conference (APWeb 2012). pp. 85-96.
  967. Murphy-Hill, E., Jiresal, R., Murphy, G. (2012). Improving Software Developers’ Fluency by Recommending Development Environment Commands. Proc. of ACM SIGSOFT 2012 FSE-20,
  968. 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, pp. 233-242.
  969. Quadrana, M. (2012). Methods for frequent pattern mining in data streams within the MOA system. Universitat Politecnica de Catalunya. Master thesis.
  970. Benzakour, A., Maabout, S., Mosbah, M., Sistiaga, M. (2012). Extraction de séquences fréquentes avec intervalles d'incertitude. Proc. EGC 2012, RNTI, pp. 213-224.
  971. Rao, S., Gupta, P. (2012). Implementing Improved Algorithm Over Apriori Data Mining Association Rule Algorithm. IJCST. Vol. 3 (1), 489-493.
  972. Mahendiran, A., Shuffett, M., Muthiah, S. Malla, R., Zhang, G. (2012). Forecasting Crime Incidents using Cluster Analysis and Sequence Mining. Research report. Virginia Tech.
  973. Ben Zakour, A. (2012). Extraction des utilisations typiques à partir de données hétérogènes historisées en vue d'optimiser la maintenance d'une flotte de véhicules. Ph.D Thesis. Université de Bordeaux, France.
  974. Faghihi, U., P. Fournier-Viger & Nkambou, R. (2012). A Computational Model for Causal Learning in Cognitive Agents. Knowledge-Based Systems, Elsevier, 30, 48-56.
  975. Zamarripa, S. et al. (2012). D4.5: 1st iteration prototype of the pattern mining module, ICT PROJECT 258109. FastFix. 15 pages.
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  977. 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.
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  979. 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.
  980. Ratchford, T. J. (2011). Creating Application Programming Interface Code Templates from Usage Patterns. Msc. Thesis. McGill University.
  981. Orellana, D., Bregt, A. K., Ligtenberg, A., Wachowicz, M. (2011). Exploring visitor movement patterns in natural recreational areas. Tourism Management, 33(3), 672–682.
  982. Roebuck, K. (2011). Data Mining: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors.
  983. Rabatel, J., Bringay, S. (2011). Extraction de motifs séquentiels contextuels. Actes des 11ièmes Journées Francophones Extraction et Gestion des Connaissances (EGC 2011), Brest, France. January, 2011 , pp. 11-22.
  984. Rabatel, J., Bringay, S., Poncelet, P. (2011). Contextual Sequential Pattern Mining. 2010 IEEE International Confersence on Data Mining Workshops. December, 2010 , pp. 981-988.
  985. 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.
  986. 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.
  987. Ben Zakour, A., Sistiaga, M., Maabout, S., Mosbah, M. (2010). Time constraints extension on frequent sequential patterns. KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 281-287
  988. Wilcox, B. (2010). Caravan: Sequential Pattern Mining in OCaml. Internal report.
  989. Baralis, E., Bruno, G., Chiusano, S., Domenici, V.C., Mahoto, N. A., Petrigni, C. (2010). Analysis of Medical Pathways by Means of Frequent Closed Sequences. Proc. of KES, 2010, pp. 418-425.

Number of citations per year

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Downloads since 2020-04-30: 14619
SPMF.jar: 16601

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.

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