Abstract
Negative sequential patterns (NSPs) capture more informative and actionable knowledge than classic positive sequential patterns (PSPs) due to the involvement of both occurring and nonoccurring behaviors and events, which can contribute to many relevant applications. However, NSP mining is nontrivial, as it involves fundamental challenges requiring distinct theoretical foundations and is not directly addressable by PSP mining. In the very limited research reported on NSP mining, a negative element constraint (NEC) is incorporated to only consider the NSPs composed of specific forms of elements (containing either positive or negative items), which results in many valuable NSPs being missed. Here, we loosen the NEC (called loose negative element constraint (LNEC)) to include partial negative elements containing both positive and negative items, which enables the discovery of more flexible patterns but incorporates significant new learning challenges, such as representing and mining complete NSPs. Accordingly, we formalize the LNEC-based NSP mining problem and propose a novel vertical NSP mining framework, VM-NSP, to efficiently mine the complete set of NSPs by a vertical representation (VR) of each sequence. An efficient bitmap-based vertical NSP mining algorithm, bM-NSP, introduces a bitmap hash table--based VR and a prefix-based negative sequential candidate generation strategy to optimize the discovery performance. VM-NSP and its implementation bM-NSP form the first VR-based approach for complete NSP mining with LNEC. Theoretical analyses and experiments confirm the performance superiority of bM-NSP on synthetic and real-life datasets w.r.t. diverse data factors, which substantially expands existing NSP mining methods toward flexible NSP discovery.
- Fahad Anwar, Ilias Petrounias, Tim Morris, and Vassilis Kodogiannis. 2010. Discovery of events with negative behavior against given sequential patterns. In Proceedings of the 2010 5th IEEE International Conferenceon Intelligent Systems. IEEE, Los Alamitos, CA, 373--378.Google ScholarCross Ref
- Sujeevan Aseervatham, Aomar Osmani, and Emmanuel Viennet. 2006. bitSPADE: A lattice-based sequential pattern mining algorithm using bitmap representation. In Proceedings of the 6th International Conference on Data Mining. IEEE, Los Alamitos, CA, 792--797.Google ScholarDigital Library
- Jay Ayres, Jason Flannick, Johannes Gehrke, and Tomi Yiu. 2002. Sequential pattern mining using a bitmap representation. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 429--435.Google ScholarDigital Library
- Philippe Besnard and Thomas Guyet. 2020. Semantics of negative sequential patterns. In Proceedings of the 24th European Conference on Artificial Intelligence (ECAI’20), Vol. 325. 1009--1015.Google Scholar
- Data Science Lab. 2020. Behavior Analytics, Computing and Informatics. Retrieved January 16, 2021 from https://datasciences.org/behavior-informatics/.Google Scholar
- Julius Borcea, Petter Brändén, and Thomas Liggett. 2009. Negative dependence and the geometry of polynomials. Journal of the American Mathematical Society 22, 2 (2009), 521--567.Google ScholarCross Ref
- Longbing Cao. 2013. Combined mining: Analyzing object and pattern relations for discovering and constructing complex yet actionable patterns. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, 2 (2013), 140--155.Google ScholarCross Ref
- Longbing Cao. 2018. Data Science Thinking: The Next Scientific, Technological and Economic Revolution. Springer International Publishing.Google Scholar
- Longbing Cao. 2020. Health and medical behavior informatics. In Biomedical Information Technology (2nd ed.). Elsevier, 735--761.Google Scholar
- Longbing Cao, Xiangjun Dong, and Zhigang Zheng. 2016. e-NSP: Efficient negative sequential pattern mining. Artificial Intelligence 235 (2016), 156--182.Google ScholarDigital Library
- Longbing Cao, Philip S. Yu, and Vipin Kumar. 2015. Nonoccurring behavior analytics: A new area. IEEE Intelligent Systems 30, 6 (2015), 4--11.Google ScholarDigital Library
- Longbing Cao, Yanchang Zhao, and Chengqi Zhang. 2008. Mining impact-targeted activity patterns in imbalanced data. IEEE Transactions on Knowledge and Data Engineering 20, 8 (2008), 1053--1066.Google ScholarDigital Library
- Ding-Ying Chiu, Yi-Hung Wu, and Arbee L. P. Chen. 2004. An efficient algorithm for mining frequent sequences by a new strategy without support counting. In Proceedings of the 2004 20th International Conference on Data Engineering. IEEE, Los Alamitos, CA, 375--386.Google Scholar
- Data Science Lab. 2020. Coupling and interaction learning. Retrieved January 16, 2021 from https://datasciences.org/coupling-learning/.Google Scholar
- Xiangjun Dong, Yongshun Gong, and Longbing Cao. 2018. F-NSP+: A fast negative sequential patterns mining method with self-adaptive data storage. Pattern Recognition 84 (2018), 13--27.Google ScholarDigital Library
- Xiangjun Dong, Yongshun Gong, and Longbing Cao. 2020. e-RNSP: An efficient method for mining repetition negative sequential patterns. IEEE Transactions on Cybernetics 50, 5 (2020), 2084--2096.Google ScholarCross Ref
- Xiangjun Dong, Yongshun Gong, and Lulin Zhao. 2014. Comparisons of typical algorithms in negative sequential pattern mining. In Proceedings of the 2014 IEEE Workshop on Electronics, Computer, and Applications. IEEE, Los Alamitos, CA, 387--390.Google ScholarCross Ref
- Xiangjun Dong, Ping Qiu, Jinhu Lü, Longbing Cao, and Tiantian Xu. 2019. Mining top-k useful negative sequential patterns via learning. IEEE Transactions on Neural Networks and Learning Systems 30, 9 (2019), 2764--2778.Google ScholarCross Ref
- Philippe Fournier-Viger, Antonio Gomariz, Manuel Campos, and Rincy Thomas. 2014. Fast vertical mining of sequential patterns using co-occurrence information. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 40--52.Google ScholarCross Ref
- Yongshun Gong, Chuanlu Liu, and Xiangjun Dong. 2015. Research on typical algorithms in negative sequential pattern mining. Open Automation and Control Systems Journal 7 (2015), 934--941.Google ScholarCross Ref
- Yongshun Gong, Tiantian Xu, Xiangjun Dong, and Guohua Lv. 2017. e-NSPFI: Efficient mining negative sequential pattern from both frequent and infrequent positive sequential patterns. International Journal of Pattern Recognition and Artificial Intelligence 31, 2 (2017), 1750002.Google ScholarCross Ref
- Thomas Guyet and René Quiniou. 2020. NegPSpan: Efficient extraction of negative sequential patterns with embedding constraints. Data Mining and Knowledge Discovery 34, 2 (2020), 563--609.Google ScholarDigital Library
- Marwan Hassani, Daniel Töws, Alfredo Cuzzocrea, and Thomas Seidl. 2019. BFSPMiner: An effective and efficient batch-free algorithm for mining sequential patterns over data streams. International Journal of Data Science and Analytics 8, 3 (2019), 223--239.Google ScholarCross Ref
- Sue-Chen Hsueh, Ming-Yen Lin, and Chien-Liang Chen. 2008. Mining negative sequential patterns for e-commerce recommendations. In Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference (APSCC’08). IEEE, Los Alamitos, CA, 1213--1218.Google ScholarDigital Library
- Jen-Wei Huang, Yong-Bin Wu, and Bijay Prasad Jaysawal. 2020. On mining progressive positive and negative sequential patterns simultaneously. Journal of Information Science and Engineering 36, 1 (2020), 145--169.Google Scholar
- B. Ozisikyilmaz, J. Pisharath, Y. Liu, R. Narayanan, W.-K. Liao, G. Memik, and A. Choudhary. [n.d.]. NU-MineBench Version 2.0 Data Set and Technical Report. Retrieved January 16, 2021 from http://cucis.ece.northwestern.edu/projects/DMS/MineBenchDownload.html.Google Scholar
- S. Kamepalli and R. Kurra. 2014. Frequent negative sequential patterns: A survey. International Journal of Computer Engineering and Technology 5, 3 (2014), 15--121.Google Scholar
- Przemysław Kazienko. 2008. Mining sequential patterns with negative conclusions. In Proceedings of the International Conference on Data Warehousing and Knowledge Discovery. 423--432.Google ScholarDigital Library
- Vinay Kumar Khare and Vedant Rastogi. 2013. Mining positive and negative sequential pattern in incremental transaction databases. International Journal of Computer Applications 71, 1 (2013), 1--5.Google Scholar
- R. Uday Kiran, Yutaka Watanobe, Bhaskar Chowdary, Koji Zettsu, Masashi Toyoda, and Masaru Kitsuregawa. 2020. Discovering maximal periodic-frequent patterns in very large temporal databases. In Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA’20). 1--10.Google ScholarCross Ref
- Yuefeng Li, Abdulmohsen Algarni, and Ning Zhong. 2010. Mining positive and negative patterns for relevance feature discovery. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 753--762.Google ScholarDigital Library
- Jefrey Lijffijt, Eirini Spyropoulou, Bo Kang, and Tijl De Bie. 2016. P-N-RMiner: A generic framework for mining interesting structured relational patterns. International Journal of Data Science and Analytics 1, 1 (2016), 61--76.Google ScholarCross Ref
- Nancy P Lin, Hung-Jen Chen, Wei-Hua Hao, Hao-En Chueh, and Chung-I. Chang. 2007. Mining negative fuzzy sequential patterns. In Proceedings of the 7th WSEAS International Conference on Simulation, Modelling, and Optimization. 52--57.Google ScholarDigital Library
- Nancy P. Lin, Wei-Hua Hao, Hung-Jen Chen, Chung-I. Chang, and Hao-En Chueh. 2007. An algorithm for mining strong negative fuzzy sequential patterns. International Journal of Computers 1, 3 (2007), 167--172.Google Scholar
- Chuanlu Liu, Xiangjun Dong, Caoyuan Li, and Yan Li. 2015. SAPNSP: Select actionable positive and negative sequential patterns based on a contribution metric. In Proceedings of the 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, Los Alamitos, CA, 811--815.Google Scholar
- MBS Online. 2020. MBS Codes. Retrieved January 16, 2021 from http://www9.health.gov.au/mbs.Google Scholar
- Carl H. Mooney and John F. Roddick. 2013. Sequential pattern mining—Approaches and algorithms. ACM Computing Surveys 45, 2 (2013), 19.Google ScholarDigital Library
- Data Science Lab. 2020. Negative Sequence Analysis. Retrieved January 16, 2021 from https://datasciences.org/negative-sequence-analysis/.Google Scholar
- Weimin Ouyang and Qinhua Huang. 2009. Mining positive and negative fuzzy multiple level sequential patterns in large transaction databases. In Proceedings of the WRI Global Congress on Intelligent Systems (GCIS’09), Vol. 1. IEEE, Los Alamitos, CA, 500--504.Google ScholarDigital Library
- Weimin Ouyang, Qinhua Huang, and Shuanghu Luo. 2008. Mining positive and negative fuzzy sequential patterns in large transaction databases. In Proceedings of the 5th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD’08), Vol. 5. IEEE, Los Alamitos, CA, 18--23.Google ScholarDigital Library
- Jian Pei, Jiawei Han, and Wei Wang. 2007. Constraint-based sequential pattern mining: The pattern-growth methods. Journal of Intelligent Information Systems 28, 2 (2007), 133--160.Google ScholarDigital Library
- Robin Pemantle. 2000. Towards a theory of negative dependence. Journal of Mathematical Physics 41, 3 (2000), 1371--1390.Google ScholarCross Ref
- Data Science Lab. 2020. Pattern Relation Analysis. Retrieved January 16, 2021 from https://datasciences.org/pattern-relation-analysis/.Google Scholar
- Akhil Ralla, P. Krishna Reddy, and Anirban Mondal. 2019. An incremental technique for mining coverage patterns in large databases. In Proceedings of the 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA’19). 211--220.Google ScholarCross Ref
- SPMF. 2020. SPMF BMS2. Retrieved January 16, 2021 from http://www.philippe-fournier-viger.com/spmf/datasets/BMS2.txt.Google Scholar
- SPMF. 2020. SPMF Dataset. Retrieved January 16, 2021 from http://www.philippe-fournier-viger.com/spmf/datasets/BMS1_spmf.Google Scholar
- SPMF. 2020. SPMF FIFA. Retrieved January 16, 2021 from http://www.philippe-fournier-viger.com/spmf/datasets/FIFA.txt.Google Scholar
- Eirini Spyropoulou and Tijl De Bie. 2014. Mining approximate multi-relational patterns. In Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics (DSAA’14). 477--483.Google ScholarCross Ref
- Ramakrishnan Srikant and Rakesh Agrawal. 1996. Mining sequential patterns: Generalizations and performance improvements. In Proceedings of the International Conference on Extending Database Technology. 1--17.Google ScholarDigital Library
- Shoujin Wang, Longbing Cao, and Yan Wang. 2019. A survey on session-based recommender systems. arXiv:1902.04864Google Scholar
- Wei Wang. 2020. Nonoccurring Sequential Behavior Analytics. Ph.D. Dissertation. University of Technology, Sydney.Google Scholar
- Wei Wang and Longbing Cao. 2019. Negative sequence analysis: A review. ACM Computing Surveys 52, 2 (2019), Article 32, 39 pages.Google ScholarDigital Library
- Tiantian Xu, Xiangjun Dong, Jianliang Xu, and Yongshun Gong. 2017. E-msNSP: Efficient negative sequential patterns mining based on multiple minimum supports. International Journal of Pattern Recognition and Artificial Intelligence 31, 02 (2017), 1750003.Google ScholarCross Ref
- Mohammed J Zaki. 2001. SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42, 1–2 (2001), 31--60.Google ScholarCross Ref
- Huaifeng Zhang, Yanchang Zhao, Longbing Cao, and Chengqi Zhang. 2008. Combined association rule mining. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’08). 1069--1074.Google ScholarCross Ref
- Yanchang Zhao, Huaifeng Zhang, Longbing Cao, Chengqi Zhang, and Hans Bohlscheid. 2009. Mining both positive and negative impact-oriented sequential rules from transactional data. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 656--663.Google ScholarDigital Library
- Yanchang Zhao, Huaifeng Zhang, Shanshan Wu, Jian Pei, Longbing Cao, Chengqi Zhang, and Hans Bohlscheid. 2009. Debt detection in social security by sequence classification using both positive and negative patterns. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 648--663.Google ScholarCross Ref
- Zhigang Zheng. 2012. Negative Sequential Pattern Mining. Ph.D. Dissertation. University of Technology, Sydney.Google Scholar
- Zhigang Zheng, Yanchang Zhao, Ziye Zuo, and Longbing Cao. 2009. Negative-GSP: An efficient method for mining negative sequential patterns. In Proceedings of the 8th Australasian Data Mining Conference—Volume 101. 63--67.Google ScholarDigital Library
- Zhigang Zheng, Yanchang Zhao, Ziye Zuo, and Longbing Cao. 2010. An efficient GA-based algorithm for mining negative sequential patterns. In Advances in Knowledge Discovery and Data Mining. Springer, 262--273.Google Scholar
Index Terms
- VM-NSP: Vertical Negative Sequential Pattern Mining with Loose Negative Element Constraints
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