Statistically Significant Discriminative Patterns Searching

Pham, Hoang Son;Gwendal Virlet;Dominique Lavenier;Alexandre Termier;et.al.
(2019) DaWaK 2019: Big Data Analytics and Knowledge Discovery — Location: Linz, Austria (26.August.2019)

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Authors
  • Pham, Hoang SonUCLouvain
    Author
  • Gwendal VirletIRISA, FRANCE
    Author
  • Dominique LavenierIRISA, FRANCE
    Author
  • Alexandre TermierIRISA, FRANCE
    Author
  • et. al.
Abstract
In this paper, we propose a novel algorithm, named SSDPS, to discover patterns in two-class datasets. The SSDPS algorithm owes its efficiency to an original enumeration strategy of the patterns, which allows to exploit some degrees of anti-monotonicity on the measures of discriminance and statistical significance. Experimental results demonstrate that the performance of the SSDPS algorithm is better than others. In addition, the number of generated patterns is much less than the number of the other algorithms. Experiment on real data also shows that SSDPS efficiently detects multiple SNPs combinations in genetic data.
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Pham, H. S., Gwendal Virlet, Dominique Lavenier, Alexandre Termier, & et al. (2019). Statistically Significant Discriminative Patterns Searching. Lecture Notes in Computational Science and Engineering, 11708(11708), 105-115. https://hdl.handle.net/2078.5/170319 (Original work published 2019)