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Mining sequential rules with itemset constraints

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Abstract

Mining sequential rules from a sequence database usually returns a set of rules with great cardinality. However, in real world applications, the end-users are often interested in a subset of sequential rules. Particularly, they may consider only rules that contain a specific set of items. The naïve strategy is to apply such itemset constraints into the post-processing step. However, such approaches require much effort and time. This paper proposes the effective methods for integrating itemset constraints into the actual mining process. We proposed two algorithms, namely MSRIC-R and MSRIC-P, to solve this problem in which MSRIC-R pushed the constraints into the rule generating phase, and MSRIC-P pushes into the pattern mining phase. Experiments show that the proposed algorithms outperform the post-processing approach.

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Acknowledgements

This research is funded by University ofScience, VNU-HCM under grant number CNTT 2021 - 01.

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Authors and Affiliations

  1. Faculty of Information Technology, Ho Chi Minh City University of Economics and Finance - UEF, Ho Chi Minh City, Vietnam

    Trang Van

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

    Trang Van

  3. Vietnam National University, Ho Chi Minh City, Vietnam

    Trang Van & Bac Le

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

    Bac Le

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  1. Trang Van
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  2. Bac Le
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Correspondence to Bac Le.

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Van, T., Le, B. Mining sequential rules with itemset constraints. Appl Intell 51, 7208–7220 (2021). https://doi.org/10.1007/s10489-020-02153-w

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