PrefixSpan

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


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

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  1. Kocheturov, A.; Pardalos, P. M.: Frequent temporal pattern mining with extended lists (2018)
  2. Le, Bac; Dinh, Duy-Tai; Huynh, Van-Nam; Nguyen, Quang-Minh; Fournier-Viger, Philippe: An efficient algorithm for hiding high utility sequential patterns (2018)
  3. 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)
  4. Aoga, John O. R.; Guns, Tias; Schaus, Pierre: Mining time-constrained sequential patterns with constraint programming (2017)
  5. Kemmar, Amina; Lebbah, Yahia; Loudni, Samir; Boizumault, Patrice; Charnois, Thierry: Prefix-projection global constraint and top-(k) approach for sequential pattern mining (2017)
  6. Zhuo, Hankz Hankui; Kambhampati, Subbarao: Model-lite planning: case-based vs. model-based approaches (2017)
  7. Zihayat, Morteza; Chen, Yan; An, Aijun: Memory-adaptive high utility sequential pattern mining over data streams (2017)
  8. Ahmed, Akiz Uddin; Ahmed, Chowdhury Farhan; Samiullah, Md.; Adnan, Nahim; Leung, Carson Kai-Sang: Mining interesting patterns from uncertain databases (2016)
  9. Boghey, Rajesh Kumar; Singh, Shailendra: A sequential tree approach for incremental sequential pattern mining (2016)
  10. Cao, Longbing; Dong, Xiangjun; Zheng, Zhigang: e-NSP: efficient negative sequential pattern mining (2016)
  11. Nakamura, Atsuyoshi; Takigawa, Ichigaku; Tosaka, Hisashi; Kudo, Mineichi; Mamitsuka, Hiroshi: Mining approximate patterns with frequent locally optimal occurrences (2016)
  12. Petitjean, Fran├žois; Li, Tao; Tatti, Nikolaj; Webb, Geoffrey I.: Skopus: mining top-(k) sequential patterns under leverage (2016)
  13. Tabaei Befrouei, Mitra; Wang, Chao; Weissenbacher, Georg: Abstraction and mining of traces to explain concurrency bugs (2016)
  14. Demetrovics, Janos; Thi, Vu Duc; Duong, Tran Huy: An algorithm to mine normalized weighted sequential patterns using a prefix-projected database (2015)
  15. Fang, Qiong; Ng, Wilfred; Feng, Jianlin; Li, Yuliang: Mining order-preserving submatrices from probabilistic matrices (2014)
  16. Lv, Mingqi; Chen, Ling; Chen, Gencai: Mining user similarity based on routine activities (2013) ioport
  17. Chen, Ling; Lv, Mingqi; Ye, Qian; Chen, Gencai; Woodward, John: A personal route prediction system based on trajectory data mining (2011) ioport
  18. Chen, Yen-Liang; Wu, Shin-Yi; Wang, Yu-Cheng: Discovering multi-label temporal patterns in sequence databases (2011) ioport
  19. Desrosiers, Christian; Galinier, Philippe; Hertz, Alain; Hansen, Pierre: Improving constrained pattern mining with first-fail-based heuristics (2011)
  20. Elzinga, Cees; Wang, Hui; Lin, Zhiwei; Kumar, Yash: Concordance and consensus (2011)

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