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Maximal Mixed-Drove Co-occurrence Patterns

Author

Listed:
  • Witold Andrzejewski

    (Poznan University of Technology)

  • Pawel Boinski

    (Poznan University of Technology)

Abstract

One of the interesting types of spatio-temporal patterns is the Mixed-Drove Co-occurrence Pattern (MDCOP), which represents types of objects frequently located together in both space and time. To discover such patterns, methods based on the well-known Apriori strategy are widely used. They involve determining multi-element MDCOPs by building them up iteratively starting with the two-element patterns and then successively adding another element in each iteration. This approach can be very costly, particularly when the data is dense enough to form patterns of significant size. In this paper, we introduce a definition of a new pattern type called a Maximal Mixed-Drove Co-occurrence Pattern. We also propose a new algorithm MAXMDCOP-Miner, which resigns from popular Apriori strategy of generating candidates and, therefore, can discover long pattern without processing all their subsets. Experiments performed on synthetic and real datasets show that MAXMDCOP-Miner has high performance, in particular for dense datasets or tasks with low user-defined thresholds of spatial or time prevalence.

Suggested Citation

  • Witold Andrzejewski & Pawel Boinski, 2023. "Maximal Mixed-Drove Co-occurrence Patterns," Information Systems Frontiers, Springer, vol. 25(5), pages 2005-2028, October.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:5:d:10.1007_s10796-022-10344-8
    DOI: 10.1007/s10796-022-10344-8
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