SPADE

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 85 articles , 1 standard article )

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  1. Kocheturov, A.; Pardalos, P. M.: Frequent temporal pattern mining with extended lists (2018)
  2. Guillame-Bert, Mathieu; Dubrawski, Artur: Classification of time sequences using graphs of temporal constraints (2017)
  3. Kemmar, Amina; Lebbah, Yahia; Loudni, Samir; Boizumault, Patrice; Charnois, Thierry: Prefix-projection global constraint and top-$k$ approach for sequential pattern mining (2017)
  4. Zhuo, Hankz Hankui; Kambhampati, Subbarao: Model-lite planning: case-based vs. model-based approaches (2017)
  5. Zihayat, Morteza; Chen, Yan; An, Aijun: Memory-adaptive high utility sequential pattern mining over data streams (2017)
  6. Ahmed, Akiz Uddin; Ahmed, Chowdhury Farhan; Samiullah, Md.; Adnan, Nahim; Leung, Carson Kai-Sang: Mining interesting patterns from uncertain databases (2016)
  7. Boghey, Rajesh Kumar; Singh, Shailendra: A sequential tree approach for incremental sequential pattern mining (2016)
  8. Cao, Longbing; Dong, Xiangjun; Zheng, Zhigang: e-NSP: efficient negative sequential pattern mining (2016)
  9. Michael Scholz: R Package clickstream: Analyzing Clickstream Data with Markov Chains (2016) not zbMATH
  10. Qu, Jun-Feng; Yuan, Lei; Huang, Yannong; Wu, Zhao: An efficient algorithm for attribute-based subsequence matching (2016)
  11. Ignatov, Dmitry I.; Gnatyshak, Dmitry V.; Kuznetsov, Sergei O.; Mirkin, Boris G.: Triadic formal concept analysis and triclustering: searching for optimal patterns (2015)
  12. Kardkovács, Zsolt T.; Kovács, Gábor: Finding sequential patterns with TF-IDF metrics in health-care databases (2014)
  13. Borgelt, Christian: Soft pattern mining in neuroscience (2013) ioport
  14. George, Aloysius; Binu, D.: DRL-prefixspan: a novel pattern growth algorithm for discovering downturn, revision and launch (DRL) sequential patterns (2012) ioport
  15. Chen, Yen-Liang; Wu, Shin-Yi; Wang, Yu-Cheng: Discovering multi-label temporal patterns in sequence databases (2011) ioport
  16. Hahsler, Michael; Chelluboina, Sudheer; Hornik, Kurt; Buchta, Christian: The arules R-package ecosystem: analyzing interesting patterns from large transaction data sets (2011)
  17. Kaneiwa, Ken; Kudo, Yasuo: A sequential pattern mining algorithm using rough set theory (2011) ioport
  18. 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
  19. Chen, Weiru; Lu, Jing; Keech, Malcolm: Discovering exclusive patterns in frequent sequences (2010)
  20. Gouda, Karam; Hassaan, Mosab; Zaki, Mohammed J.: PRISM: an effective approach for frequent sequence mining via prime-block encoding (2010)

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