PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology of Apriori which may substantially reduce the number of combinations to be examined. However, Apriori still encounters problems when a sequence database is large and/or when sequential patterns to be mined are numerous and/or long. In this paper, we propose a novel sequential pattern mining method, called PrefixSpan (i.e., Prefix-projected Sequential pattern mining), which explores prefix-projection in sequential pattern mining. PrefixSpan mines the complete set of patterns but greatly reduces the efforts of candidate subsequence generation. Moreover, prefix-projection substantially reduces the size of projected databases and leads to efficient processing. Our performance study shows that PrefixSpan outperforms both the Apriori-based GSP algorithm and another recently proposed method, FreeSpan, in mining large sequence databases.PrefixSpan

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  1. Fournier-Viger, Philippe; Yang, Peng; Kiran, Rage Uday; Ventura, Sebastian; Luna, José María: Mining local periodic patterns in a discrete sequence (2021)
  2. Tsumoto, Shusaku; Hirano, Shoji; Kimura, Tomohiro; Iwata, Haruko: Mining clinical process from hospital information system: a granular computing approach (2021)
  3. Yoshida, Tomoki; Takeuchi, Ichiro; Karasuyama, Masayuki: Distance metric learning for graph structured data (2021)
  4. Guyet, Thomas; Quiniou, René: NegSPan: efficient extraction of negative sequential patterns with embedding constraints (2020)
  5. Cule, Boris; Feremans, Len; Goethals, Bart: Efficiently mining cohesion-based patterns and rules in event sequences (2019)
  6. Fournier-Viger, Philippe; Li, Zhitian; Lin, Jerry Chun-Wei; Kiran, Rage Uday; Fujita, Hamido: Efficient algorithms to identify periodic patterns in multiple sequences (2019)
  7. Kocheturov, A.; Pardalos, P. M.: Frequent temporal pattern mining with extended lists (2018)
  8. Le, Bac; Dinh, Duy-Tai; Huynh, Van-Nam; Nguyen, Quang-Minh; Fournier-Viger, Philippe: An efficient algorithm for hiding high utility sequential patterns (2018)
  9. 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)
  10. Liu, Junqiang; Zhang, Xingxing; Fung, Benjamin C. M.; Li, Jiuyong; Iqbal, Farkhund: Opportunistic mining of top-(n) high utility patterns (2018)
  11. Aoga, John O. R.; Guns, Tias; Schaus, Pierre: Mining time-constrained sequential patterns with constraint programming (2017)
  12. Kemmar, Amina; Lebbah, Yahia; Loudni, Samir; Boizumault, Patrice; Charnois, Thierry: Prefix-projection global constraint and top-(k) approach for sequential pattern mining (2017)
  13. Zhuo, Hankz Hankui; Kambhampati, Subbarao: Model-lite planning: case-based vs. model-based approaches (2017)
  14. Zihayat, Morteza; Chen, Yan; An, Aijun: Memory-adaptive high utility sequential pattern mining over data streams (2017)
  15. Ahmed, Akiz Uddin; Ahmed, Chowdhury Farhan; Samiullah, Md.; Adnan, Nahim; Leung, Carson Kai-Sang: Mining interesting patterns from uncertain databases (2016)
  16. Boghey, Rajesh Kumar; Singh, Shailendra: A sequential tree approach for incremental sequential pattern mining (2016)
  17. Cao, Longbing; Dong, Xiangjun; Zheng, Zhigang: e-NSP: efficient negative sequential pattern mining (2016)
  18. Nakamura, Atsuyoshi; Takigawa, Ichigaku; Tosaka, Hisashi; Kudo, Mineichi; Mamitsuka, Hiroshi: Mining approximate patterns with frequent locally optimal occurrences (2016)
  19. Petitjean, François; Li, Tao; Tatti, Nikolaj; Webb, Geoffrey I.: Skopus: mining top-(k) sequential patterns under leverage (2016)
  20. Tabaei Befrouei, Mitra; Wang, Chao; Weissenbacher, Georg: Abstraction and mining of traces to explain concurrency bugs (2016)

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