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Sampling frequent and minimal boolean patterns: theory and application in classification

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Abstract

We tackle the challenging problem of mining the simplest Boolean patterns from categorical datasets. Instead of complete enumeration, which is typically infeasible for this class of patterns, we develop effective sampling methods to extract a representative subset of the minimal Boolean patterns in disjunctive normal form (DNF). We propose a novel theoretical characterization of the minimal DNF expressions, which allows us to prune the pattern search space effectively. Our approach can provide a near-uniform sample of the minimal DNF patterns. We perform an extensive set of experiments to demonstrate the effectiveness of our sampling method. We also show that minimal DNF patterns make effective features for classification.

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Acknowledgments

This work was supported in part by NSF Awards CCF-1240646 and IIS-1302231.

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Authors and Affiliations

  1. Rensselaer Polytechnic Institute, Troy, NY, USA

    Geng Li & Mohammed J. Zaki

  2. Qatar Computing Research Institute, Doha, Qatar

    Mohammed J. Zaki

Authors
  1. Geng Li
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  2. Mohammed J. Zaki
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Correspondence to Mohammed J. Zaki.

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Responsible editor: Bart Goethals.

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Li, G., Zaki, M.J. Sampling frequent and minimal boolean patterns: theory and application in classification. Data Min Knowl Disc 30, 181–225 (2016). https://doi.org/10.1007/s10618-015-0409-y

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  • DOI: https://doi.org/10.1007/s10618-015-0409-y

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