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Mining diverse sets of patterns with constraint programming using the pairwise Jaccard similarity relaxation

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Abstract

In recent years, pattern mining has evolved from a slow-moving, repetitive three-step process to a much more agile and iterative/user-centric mining model. A crucial element of this framework is the capability to rapidly provide a set of diverse patterns to the user. This paper proposes a pattern mining approach based on constraint programming that incorporates a non-redundancy/diversity constraint into closed pattern enumeration. The level of diversity is controlled through a threshold on the maximum pairwise Jaccard similarity of pattern occurrences. We show that the Jaccard measure does not have nice (anti-)monotonicity properties w.r.t. the general-to-specific enumeration. To address this limitation, we propose anti-monotonic lower and upper-bound relaxations of the Jaccard similarity with nice pruning-enabling properties, and connect the final results to the original Jaccard Index. To evaluate the effectiveness of our relaxations, we conduct a comprehensive comparison against several existing pattern mining techniques designed to control redundancy. Experimental results illustrate that our approach provides an effective solution for mining diverse itemsets, showing competitive performance in both runtime and flexibility.

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Notes

  1. Opposed to more rigid search in classical pattern mining algorithms, which often rely on exploiting the properties of a particular interestingness measure.

  2. https://github.com/lobnury/ClosedDiversity/tree/master/Suppl_Material

  3. \(\kappa \in (0, 1)\) denotes the sampling error tolerance.

  4. https://github.com/lobnury/ClosedDiversity/tree/master/Suppl_Material

  5. The source code is publicly available at https://github.com/lobnury/ClosedDiversity

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Authors and Affiliations

  1. Lab. LITIO, Université Oran1, Oran, 31000, Algeria

    Noureddine Aribi & Yahia Lebbah

  2. TASC (LS2N-CNRS), IMT Atlantique, Nantes, 44307, France

    Arnold Hien & Samir Loudni

  3. CNRS – UMR GREYC, Normandie University, Caen, 14000, France

    Arnold Hien, Abdelkader Ouali & Albrecht Zimmermann

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Hien, A., Aribi, N., Loudni, S. et al. Mining diverse sets of patterns with constraint programming using the pairwise Jaccard similarity relaxation. Constraints 29, 80–111 (2024). https://doi.org/10.1007/s10601-024-09373-8

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