A heuristic to predict the optimal pattern-growth direction for the pattern growth-based sequential pattern mining approach

  • Authors

    • Kenmogne Edith Belise Faculty of Science, Department of Mathematics and Computer Science, LIFA, Po. Box. 67 Dschang,Cameroon http://orcid.org/0000-0002-0149-4167
    • Nkambou Roger
    • Tadmon Calvin
    • Engelbert Mephu Nguifo
    2017-06-04
    https://doi.org/10.14419/jacst.v6i2.7011
  • Sequence Mining, Sequential Pattern, Frequent Pattern, Pattern-Growth Direction, Heuristic, Prefixspan, Suffixspan.

  • Abstract

    Sequential pattern mining is an efficient technique for discovering recurring structures or patterns from very large datasets, with a very large field of applications. It aims at extracting a set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan, PrefixSpan and their further extensions. The performances of these algorithms depend on how patterns grow. Because of this, we introduce a heuristic to predict the optimal pattern-growth direction, i.e. the pattern-growth direction leading to the best performance in terms of runtime and memory usage. Then, we perform a number of experimentations on both real-life and synthetic datasets to test the heuristic. The performance analysis of these experimentations show that the heuristic prediction is reliable in general.

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  • How to Cite

    Edith Belise, K., Roger, N., Calvin, T., & Nguifo, E. M. (2017). A heuristic to predict the optimal pattern-growth direction for the pattern growth-based sequential pattern mining approach. Journal of Advanced Computer Science & Technology (JACST), 6(2), 20-32. https://doi.org/10.14419/jacst.v6i2.7011

    Received date: 2016-11-24

    Accepted date: 2017-01-02

    Published date: 2017-06-04