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Efficient Implementations for UWEP Incremental Frequent Itemset Mining Algorithm

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  • Mehmet Bicer

    (Graduate Center, City University of New York, USA)

  • Daniel Indictor

    (Columbia University, USA)

  • Ryan Yang

    (Massachusetts Institute of Technology, USA)

  • Xiaowen Zhang

    (College of Staten Island, City University of New York, USA)

Abstract

Association rule mining is a common technique used in discovering interesting frequent patterns in data acquired in various application domains. The search space combinatorically explodes as the size of the data increases. Furthermore, the introduction of new data can invalidate old frequent patterns and introduce new ones. Hence, while finding the association rules efficiently is an important problem, maintaining and updating them is also crucial. Several algorithms have been introduced to find the association rules efficiently. One of them is Apriori. There are also algorithms written to update or maintain the existing association rules. Update with early pruning (UWEP) is one such algorithm. In this paper, the authors propose that in certain conditions it is preferable to use an incremental algorithm as opposed to the classic Apriori algorithm. They also propose new implementation techniques and improvements to the original UWEP paper in an algorithm we call UWEP2. These include the use of memorization and lazy evaluation to reduce scans of the dataset.

Suggested Citation

  • Mehmet Bicer & Daniel Indictor & Ryan Yang & Xiaowen Zhang, 2021. "Efficient Implementations for UWEP Incremental Frequent Itemset Mining Algorithm," International Journal of Applied Logistics (IJAL), IGI Global, vol. 11(1), pages 18-37, January.
    • Handle: RePEc:igg:jal000:v:11:y:2021:i:1:p:18-37
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