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Mining sequential patterns with itemset constraints

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Abstract

Mining sequential patterns is used to discover all the frequent sequences in a sequence database. However, the mining may return a huge number of patterns, while the users are only interested in a particular subset of these. In this paper, we consider the problem of mining sequential patterns with itemset constraints. In order to solve this problem, we propose a new algorithm named MSPIC-DBV, which is a pattern-growth algorithm that uses prefixes and dynamic bit vectors. This algorithm prunes the search space at the beginning and during the mining process. Moreover, it reduces the number of candidates that need to be checked. The experimental results show that the proposed algorithm outperforms the previous methods.

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Acknowledgements

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant Number 102.05-2015.07.

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Authors and Affiliations

  1. Faculty of Information Technology, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Trang Van & Bay Vo

  2. Faculty of Computer Science, VNUHCM, University of Information Technology, Ho Chi Minh City, Vietnam

    Trang Van

  3. Faculty of Information Technology, VNUHCM, University of Science, Ho Chi Minh City, Vietnam

    Bac Le

Authors
  1. Trang Van
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  2. Bay Vo
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  3. Bac Le
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Correspondence to Bac Le.

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Van, T., Vo, B. & Le, B. Mining sequential patterns with itemset constraints. Knowl Inf Syst 57, 311–330 (2018). https://doi.org/10.1007/s10115-018-1161-6

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  • DOI: https://doi.org/10.1007/s10115-018-1161-6

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