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Efficient mining of sequential patterns with time constraints by delimited pattern growth

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Abstract

An active research topic in data mining is the discovery of sequential patterns, which finds all frequent subsequences in a sequence database. The generalized sequential pattern (GSP) algorithm was proposed to solve the mining of sequential patterns with time constraints, such as time gaps and sliding time windows. Recent studies indicate that the pattern-growth methodology could speed up sequence mining. However, the capabilities to mine sequential patterns with time constraints were previously available only within the Apriori framework. Therefore, we propose the DELISP (delimited sequential pattern) approach to provide the capabilities within the pattern-growth methodology. DELISP features in reducing the size of projected databases by bounded and windowed projection techniques. Bounded projection keeps only time-gap valid subsequences and windowed projection saves nonredundant subsequences satisfying the sliding time-window constraint. Furthermore, the delimited growth technique directly generates constraint-satisfactory patterns and speeds up the pattern growing process. The comprehensive experiments conducted show that DELISP has good scalability and outperforms the well-known GSP algorithm in the discovery of sequential patterns with time constraints.

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Authors and Affiliations

  1. Department of Information Engineering and Computer Science, Feng Chia University, Taiwan

    Ming-Yen Lin

  2. Department of Computer Science and Information Engineering, National Chiao Tung University, Taiwan

    Suh-Yin Lee

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  1. Ming-Yen Lin
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  2. Suh-Yin Lee
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Correspondence to Ming-Yen Lin.

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Lin, MY., Lee, SY. Efficient mining of sequential patterns with time constraints by delimited pattern growth. Knowl Inf Syst 7, 499–514 (2005). https://doi.org/10.1007/s10115-004-0182-5

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  • DOI: https://doi.org/10.1007/s10115-004-0182-5

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