CLOSET: an efficient logarithm for mining frequent closed itemsets. Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces the number of rules to be presented. In this paper, we propose an efficient algorithm, CLOSET, for mining closed itemsets, with the development of three techniques: (1) applying a compressed, frequent pattern tree FP-tree structure for mining closed itemsets without candidate generation, (2) developing a single prefix path compression technique to identify frequent closed itemsets quickly, and (3) exploring a partition-based projection mechanism for scalable mining in large databases. Our performance study shows that CLOSET is efficient and scalable over large databases, and is faster than the previously proposed methods.

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  1. Paramonov, Sergey; Stepanova, Daria; Miettinen, Pauli: Hybrid ASP-based approach to pattern mining (2019)
  2. Djenouri, Youcef; Belhadi, Asma; Fournier-Viger, Philippe; Lin, Jerry Chun-Wei: Fast and effective cluster-based information retrieval using frequent closed itemsets (2018)
  3. Wang, Lizhen; Bao, Xuguang; Chen, Hongmei; Cao, Longbing: Effective lossless condensed representation and discovery of spatial co-location patterns (2018)
  4. Shin, Se Jung; Lee, Dae Su; Lee, Won Suk: CP-tree: an adaptive synopsis structure for compressing frequent itemsets over online data streams (2014)
  5. Szathmary, Laszlo; Valtchev, Petko; Napoli, Amedeo; Godin, Robert; Boc, Alix; Makarenkov, Vladimir: A fast compound algorithm for mining generators, closed itemsets, and computing links between equivalence classes (2014)
  6. Garriga, Gemma C.; Khardon, Roni; De Raedt, Luc: Mining closed patterns in relational, graph and network data (2013)
  7. Deng, Zhihong; Wang, Zhonghui; Jiang, Jiajian: A new algorithm for fast mining frequent itemsets using N-lists (2012)
  8. Farzanyar, Zahra; Kangavari, Mohammadreza; Cercone, Nick: MAX-FISM: mining (recently) maximal frequent itemsets over data streams using the sliding window model (2012)
  9. Kenig, Batya; Tassa, Tamir: A practical approximation algorithm for optimal (k)-anonymity (2012)
  10. Guns, Tias; Nijssen, Siegfried; De Raedt, Luc: Itemset mining: a constraint programming perspective (2011)
  11. Kim, Chulyun; Miao, Hui; Shim, Kyuseok: CATCH: a detecting algorithm for coalition attacks of hit inflation in internet advertising (2011) ioport
  12. Kwuida, Léonard; Schmidt, Stefan E.: Valuations and closure operators on finite lattices (2011)
  13. Lee, Anthony J. T.; Tsao, Wen-Kwang; Chen, Po-Yin; Lin, Ming-Chih; Yang, Shih-Hui: Mining frequent closed patterns in pointset databases (2010) ioport
  14. Gidófalvi, Győző; Pedersen, Torben Bach: Mining long, sharable patterns in trajectories of moving objects (2009) ioport
  15. Liu, Hongyan; Wang, Xiaoyu; He, Jun; Han, Jiawei; Xin, Dong; Shao, Zheng: Top-down mining of frequent closed patterns from very high dimensional data (2009)
  16. Pandey, Gaurav; Chawla, Sanjay; Poon, Simon; Arunasalam, Bavani; Davis, Joseph G.: Association rules network: definition and applications (2009)
  17. Zeng, Xinghuo; Pei, Jian; Wang, Ke; Li, Jinyan: PADS: a simple yet effective pattern-aware dynamic search method for fast maximal frequent pattern mining (2009) ioport
  18. Atzori, Maurizio; Bonchi, Francesco; Giannotti, Fosca; Pedreschi, Dino: Anonymity preserving pattern discovery (2008) ioport
  19. Chaoji, Vineet; Hasan, Mohammad Al; Salem, Saeed; Zaki, Mohammed J.: An integrated, generic approach to pattern mining: data mining template library (2008) ioport
  20. Cheng, James; Ke, Yiping; Ng, Wilfred: Maintaining frequent closed itemsets over a sliding window (2008) ioport

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