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An efficient algorithm for mining top-rank-k frequent patterns

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Abstract

Mining top-rank-k frequent patterns is a popular data mining task, which consists of discovering the patterns in a transaction database that belong to the k first ranks in terms of support. Although, several algorithms have been proposed for this task, it remains computationally expensive. To address this issue, this paper proposes a novel algorithm named BTK. It relies on a novel tree structure named TB-tree to store crucial information about frequent patterns. Moreover, BTK employs a new B-list structure to store information about patterns, and relies on subsume indexes to reduce the search space and speed up the discovery of top-rank-k frequent patterns. BTK also uses an early pruning strategy and an effective threshold raising mechanism. Additionally, BTK introduces two efficient procedures for respectively generating subsume indexes and intersecting B-lists. Extensive experiments were conducted on several datasets to evaluate the efficiency of the proposed algorithm. Results show that BTK is highly efficient and competitive.

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Notes

  1. Downloaded from FIMI repository http://fimi.ua.ac.be/data/.

  2. Downloaded from http://cgi.csc.liv.ac.uk/~frans/KDD/Software/LUCS-KDD-DataGen/generator.html.

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Acknowledgments

This research was funded by the National Natural Science Foundation of China (Grant Nos. 61133005, 61432005, 61370095, 61472124, 61202109, and 61472126) and the International Science & Technology Cooperation Program of China (Grant No. 2015DFA11240,2014DFBS0010). T-L. Dam was also partially supported by science research fund of Hanoi University of Industry, Hanoi, Vietnam.

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Thu-Lan Dam, Kenli Li & Quang-Huy Duong

  2. Faculty of Information Technology, Hanoi University of Industry, Hanoi, Vietnam

    Thu-Lan Dam

  3. CIC of HPC, National University of Defense Technology, Changsha, 410073, China

    Thu-Lan Dam & Kenli Li

  4. National Supercomputing Center in Changsha, Changsha, Hunan, 410082, China

    Kenli Li

  5. School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, 518055, China

    Philippe Fournier-Viger

Authors
  1. Thu-Lan Dam
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  2. Kenli Li
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  3. Philippe Fournier-Viger
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  4. Quang-Huy Duong
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Corresponding author

Correspondence to Kenli Li.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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The authors declare that they have no conflict of interest.

Funding

This research was funded by the National Natural Science Foundation of China (Grant Nos. 61133005, 61432005, 61370095, 61472124, 61202109, and 61472126) and the International Science & Technology Cooperation Program of China (Grant No. 2015DFA11240). T-L. Dam was also partially supported by science research fund of Hanoi University of Industry, Hanoi, Vietnam.

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Dam, TL., Li, K., Fournier-Viger, P. et al. An efficient algorithm for mining top-rank-k frequent patterns. Appl Intell 45, 96–111 (2016). https://doi.org/10.1007/s10489-015-0748-9

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  • DOI: https://doi.org/10.1007/s10489-015-0748-9

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