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.

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  1. Zhuo, Hankz Hankui; Kambhampati, Subbarao: Model-lite planning: case-based vs. model-based approaches (2017)
  2. Boghey, Rajesh Kumar; Singh, Shailendra: A sequential tree approach for incremental sequential pattern mining (2016)
  3. Cao, Longbing; Dong, Xiangjun; Zheng, Zhigang: e-NSP: efficient negative sequential pattern mining (2016)
  4. Ignatov, Dmitry I.; Gnatyshak, Dmitry V.; Kuznetsov, Sergei O.; Mirkin, Boris G.: Triadic formal concept analysis and triclustering: searching for optimal patterns (2015)
  5. Kardkovács, Zsolt T.; Kovács, Gábor: Finding sequential patterns with TF-IDF metrics in health-care databases (2014)
  6. Borgelt, Christian: Soft pattern mining in neuroscience (2013) ioport
  7. George, Aloysius; Binu, D.: DRL-prefixspan: a novel pattern growth algorithm for discovering downturn, revision and launch (DRL) sequential patterns (2012) ioport
  8. Chen, Yen-Liang; Wu, Shin-Yi; Wang, Yu-Cheng: Discovering multi-label temporal patterns in sequence databases (2011) ioport
  9. Hahsler, Michael; Chelluboina, Sudheer; Hornik, Kurt; Buchta, Christian: The arules R-package ecosystem: analyzing interesting patterns from large transaction data sets (2011)
  10. Kaneiwa, Ken; Kudo, Yasuo: A sequential pattern mining algorithm using rough set theory (2011) ioport
  11. 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
  12. Chen, Weiru; Lu, Jing; Keech, Malcolm: Discovering exclusive patterns in frequent sequences (2010)
  13. Gouda, Karam; Hassaan, Mosab; Zaki, Mohammed J.: PRISM: an effective approach for frequent sequence mining via prime-block encoding (2010)
  14. Huang, Tony Cheng-Kui: Knowledge gathering of fuzzy multi-time-interval sequential patterns (2010) ioport
  15. Kong, Xiaoxiao; Wei, Qiang; Chen, Guoqing: An approach to discovering multi-temporal patterns and its application to financial databases (2010) ioport
  16. Loekito, Elsa; Bailey, James; Pei, Jian: A binary decision diagram based approach for mining frequent subsequences (2010) ioport
  17. Vinceslas, Lionel; Symphor, Jean-Emile; Mancheron, Alban; Poncelet, Pascal: SPAMS: A novel incremental approach for sequential pattern mining in data streams (2010)
  18. Yun, Unil; Ryu, Keun Ho: Discovering important sequential patterns with length-decreasing weighted support constraints (2010)
  19. Chiu, Ding-Ying; Wu, Yi-Hung; Chen, Arbee L.P.: Efficient frequent sequence mining by a dynamic strategy switching algorithm (2009) ioport
  20. Ezeife, C.I.; Liu, Yi: Fast incremental mining of web sequential patterns with PLWAP tree (2009) ioport

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