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 89 articles , 1 standard article )

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  1. Dia, Diyé; Kahn, Giacomo; Labernia, Fabien; Loiseau, Yannick; Raynaud, Olivier: A closed sets based learning classifier for implicit authentication in web browsing (2020)
  2. Guyet, Thomas; Quiniou, René: NegSPan: efficient extraction of negative sequential patterns with embedding constraints (2020)
  3. Cule, Boris; Feremans, Len; Goethals, Bart: Efficiently mining cohesion-based patterns and rules in event sequences (2019)
  4. Kocheturov, A.; Pardalos, P. M.: Frequent temporal pattern mining with extended lists (2018)
  5. Li, Guang; Liu, Kai; Ding, Wenwen; Cheng, Fei; Chen, Boyang: Key-skeleton-pattern mining on 3D skeletons represented by Lie group for action recognition (2018)
  6. Kemmar, Amina; Lebbah, Yahia; Loudni, Samir; Boizumault, Patrice; Charnois, Thierry: Prefix-projection global constraint and top-(k) approach for sequential pattern mining (2017)
  7. Zhuo, Hankz Hankui; Kambhampati, Subbarao: Model-lite planning: case-based vs. model-based approaches (2017)
  8. Zihayat, Morteza; Chen, Yan; An, Aijun: Memory-adaptive high utility sequential pattern mining over data streams (2017)
  9. Ahmed, Akiz Uddin; Ahmed, Chowdhury Farhan; Samiullah, Md.; Adnan, Nahim; Leung, Carson Kai-Sang: Mining interesting patterns from uncertain databases (2016)
  10. Bhuiyan, Mansurul; Hasan, Mohammad Al: Interactive knowledge discovery from hidden data through sampling of frequent patterns (2016)
  11. Boghey, Rajesh Kumar; Singh, Shailendra: A sequential tree approach for incremental sequential pattern mining (2016)
  12. Cao, Longbing; Dong, Xiangjun; Zheng, Zhigang: e-NSP: efficient negative sequential pattern mining (2016)
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  16. Kardkovács, Zsolt T.; Kovács, Gábor: Finding sequential patterns with TF-IDF metrics in health-care databases (2014)
  17. Borgelt, Christian: Soft pattern mining in neuroscience (2013) ioport
  18. George, Aloysius; Binu, D.: DRL-prefixspan: a novel pattern growth algorithm for discovering downturn, revision and launch (DRL) sequential patterns (2012) ioport
  19. Chen, Yen-Liang; Wu, Shin-Yi; Wang, Yu-Cheng: Discovering multi-label temporal patterns in sequence databases (2011) ioport
  20. Hahsler, Michael; Chelluboina, Sudheer; Hornik, Kurt; Buchta, Christian: The arules R-package ecosystem: analyzing interesting patterns from large transaction data sets (2011)

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