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Discovering fuzzy time-interval sequential patterns in sequence databases.

Yen-Liang Chen1, Tony Cheng-Kui Huang

  • 1Department of Information Management, National Central University, Chung-Li, Taiwan. ylchen@mgt.ncu.edu.tw

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 26, 2005
PubMed
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This study introduces fuzzy time-interval sequential patterns to address limitations in analyzing item order and time gaps. New algorithms, FTI-Apriori and FTI-PrefixSpan, efficiently discover these patterns, with FTI-PrefixSpan showing superior performance.

Area of Science:

  • Data Mining
  • Pattern Recognition
  • Computational Intelligence

Background:

  • Sequential pattern mining identifies frequently occurring ordered itemsets in databases.
  • Time-interval sequential patterns extend this by incorporating time gaps between items.
  • Existing methods face a 'sharp boundary problem' when time intervals near predefined ranges.

Purpose of the Study:

  • To introduce fuzzy time-interval sequential patterns to overcome the sharp boundary problem.
  • To develop efficient algorithms for discovering these fuzzy patterns.
  • To compare the performance of the developed algorithms.

Main Methods:

  • Extension of sequential pattern mining using fuzzy set theory.
  • Development of the fuzzy time interval (FTI)-Apriori algorithm.

Related Experiment Videos

  • Development of the FTI-PrefixSpan algorithm for mining fuzzy time-interval sequential patterns.

    • Main Results:

    • Fuzzy time-interval sequential patterns effectively handle time intervals without sharp boundaries.
    • The FTI-PrefixSpan algorithm demonstrates superior computational efficiency compared to FTI-Apriori.
    • FTI-PrefixSpan exhibits better scalability across various parameters.

    Conclusions:

    • Fuzzy set theory provides a robust framework for time-interval sequential pattern mining.
    • The FTI-PrefixSpan algorithm is a more efficient and scalable solution for discovering fuzzy time-interval sequential patterns.
    • This approach enhances the analysis of temporal relationships in sequential data.