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Discovering Productive Periodic Frequent Patterns in Transactional Databases

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  • Vincent Mwintieru Nofong

    (University of South Australia)

Abstract

Periodic frequent pattern mining is an important data mining task for various decision making. However, it often presents a large number of periodic frequent patterns, most of which are not useful as their periodicities are due to random occurrence of uncorrelated items. Such periodic frequent patterns would most often be detrimental in decision making where correlations between the items of periodic frequent patterns are vital. To enable mine the periodic frequent patterns with correlated items, we employ a correlation test on periodic frequent patterns and introduce the productive periodic frequent patterns as the set of periodic frequent patterns with correlated items. We finally develop the productive periodic frequent pattern (PPFP) framework for mining our introduced productive periodic frequent patterns. PPFP is efficient and the productiveness measure removes the periodic frequent patterns with uncorrelated items.

Suggested Citation

  • Vincent Mwintieru Nofong, 2016. "Discovering Productive Periodic Frequent Patterns in Transactional Databases," Annals of Data Science, Springer, vol. 3(3), pages 235-249, September.
    • Handle: RePEc:spr:aodasc:v:3:y:2016:i:3:d:10.1007_s40745-016-0078-8
    DOI: 10.1007/s40745-016-0078-8
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