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Mining frequent closed inter-sequence patterns efficiently using dynamic bit vectors

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Abstract

Mining frequent sequences is a critical stage before rule generation for sequence databases. Currently, there are two main ways for mining frequent sequences, namely intra-sequence mining and inter-sequence mining. Inter-sequence mining is more attractive than intra-sequence mining because it considers the relationship between sequences in transactions. However, mining all possible frequent inter-sequences takes a long time and requires a lot of memory. Mining frequent closed inter-sequences is efficient because such sequences are compact, and only the necessary information is maintained. CISP-Miner was proposed for mining frequent closed inter-sequence patterns, but it consumes a lot of memory since many closed patterns are mined. This paper proposes an algorithm called ClosedISP for mining frequent closed inter-sequence patterns. The proposed algorithm uses a checking scheme for early eliminating and checking closed patterns without candidate maintenance. ClosedISP uses a dynamic bit vector that combines transaction information to compress data. In addition, ClosedISP adopts a prefix tree and a depth-first search order to reduce the search space and generate non-redundant sequential rules efficiently. Experiments were conducted to compare the proposed algorithm with CISP-Miner to demonstrate the effectiveness of the proposed algorithm in terms of runtime and memory usage.

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Acknowledgments

This work was funded by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant no. 102.05-2013.20.

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Authors and Affiliations

  1. Department of Computer Science, VNU - Ho Chi Minh - University of Science, Ho Chi Minh, Vietnam

    Bac Le

  2. Faculty of Information Technology, Information Technology College, Ho Chi Minh, Vietnam

    Minh-Thai Tran

  3. Division of Data Science, Ton Duc Thang University, Ho Chi Minh, Vietnam

    Bay Vo

  4. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh, Vietnam

    Bay Vo

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  1. Bac Le
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  2. Minh-Thai Tran
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  3. Bay Vo
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Correspondence to Bay Vo.

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Le, B., Tran, MT. & Vo, B. Mining frequent closed inter-sequence patterns efficiently using dynamic bit vectors. Appl Intell 43, 74–84 (2015). https://doi.org/10.1007/s10489-014-0630-1

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