In this paper we present SPADE, a new algorithm for fast discovery of Sequential Patterns. The existing solutions to this problem make repeated database scans, and use complex hash structures which have poor locality. SPADE utilizes combinatorial properties to decompose the original problem into smaller sub-problems, that can be independently solved in main-memory using efficient lattice search techniques, and using simple join operations. All sequences are discovered in only three database scans. Experiments show that SPADE outperforms the best previous algorithm by a factor of two, and by an order of magnitude with some pre-processed data. It also has linear scalability with respect to the number of input-sequences, and a number of other database parameters. Finally, we discuss how the results of sequence mining can be applied in a real application domain.

References in zbMATH (referenced in 76 articles , 1 standard article )

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  1. Boghey, Rajesh Kumar; Singh, Shailendra: A sequential tree approach for incremental sequential pattern mining (2016)
  2. Cao, Longbing; Dong, Xiangjun; Zheng, Zhigang: e-NSP: efficient negative sequential pattern mining (2016)
  3. Ignatov, Dmitry I.; Gnatyshak, Dmitry V.; Kuznetsov, Sergei O.; Mirkin, Boris G.: Triadic formal concept analysis and triclustering: searching for optimal patterns (2015)
  4. Kardkovács, Zsolt T.; Kovács, Gábor: Finding sequential patterns with TF-IDF metrics in health-care databases (2014)
  5. Borgelt, Christian: Soft pattern mining in neuroscience (2013) ioport
  6. Chen, Yen-Liang; Wu, Shin-Yi; Wang, Yu-Cheng: Discovering multi-label temporal patterns in sequence databases (2011) ioport
  7. Hahsler, Michael; Chelluboina, Sudheer; Hornik, Kurt; Buchta, Christian: The arules R-package ecosystem: analyzing interesting patterns from large transaction data sets (2011)
  8. Kaneiwa, Ken; Kudo, Yasuo: A sequential pattern mining algorithm using rough set theory (2011) ioport
  9. Mcgovern, Amy; Rosendahl, Derek H.; Brown, Rodger A.; Droegemeier, Kelvin K.: Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction (2011) ioport
  10. Chen, Weiru; Lu, Jing; Keech, Malcolm: Discovering exclusive patterns in frequent sequences (2010)
  11. Gouda, Karam; Hassaan, Mosab; Zaki, Mohammed J.: PRISM: an effective approach for frequent sequence mining via prime-block encoding (2010)
  12. Huang, Tony Cheng-Kui: Knowledge gathering of fuzzy multi-time-interval sequential patterns (2010) ioport
  13. Kong, Xiaoxiao; Wei, Qiang; Chen, Guoqing: An approach to discovering multi-temporal patterns and its application to financial databases (2010) ioport
  14. Loekito, Elsa; Bailey, James; Pei, Jian: A binary decision diagram based approach for mining frequent subsequences (2010) ioport
  15. Vinceslas, Lionel; Symphor, Jean-Emile; Mancheron, Alban; Poncelet, Pascal: SPAMS: A novel incremental approach for sequential pattern mining in data streams (2010)
  16. Yun, Unil; Ryu, Keun Ho: Discovering important sequential patterns with length-decreasing weighted support constraints (2010)
  17. Chiu, Ding-Ying; Wu, Yi-Hung; Chen, Arbee L.P.: Efficient frequent sequence mining by a dynamic strategy switching algorithm (2009) ioport
  18. Ezeife, C.I.; Liu, Yi: Fast incremental mining of web sequential patterns with PLWAP tree (2009) ioport
  19. Huang, Tony Cheng-Kui: Developing an efficient knowledge discovering model for mining fuzzy multi-level sequential patterns in sequence databases (2009) ioport
  20. Jacquemont, Stéphanie; Jacquenet, François; Sebban, Marc: Mining probabilistic automata: a statistical view of sequential pattern mining (2009) ioport

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