Statistically Significant Discriminative Patterns Searching - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Statistically Significant Discriminative Patterns Searching


In this paper, we propose a novel algorithm, named SSDPS, to discover patterns in two-class datasets. The SSDPS algorithm owes its eciency 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 eciently detects multiple SNPs combinations in genetic data.
Fichier principal
Vignette du fichier
paper_SSDPS_DaWak19_final.pdf (304.38 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-02190793 , version 1 (22-07-2019)



Hoang Son Pham, Gwendal Virlet, Dominique Lavenier, Alexandre Termier. Statistically Significant Discriminative Patterns Searching. DaWaK 2019 - 21st International Conference on Big Data Analytics and Knowledge Discovery, Aug 2019, Linz, Austria. pp.105-115, ⟨10.1007/978-3-030-27520-4_8⟩. ⟨hal-02190793⟩
115 View
160 Download



Gmail Mastodon Facebook X LinkedIn More