  Discriminative pattern mining is an essential task of data mining. This task
aims to discover patterns which occur more frequently in a class than other
classes in a class-labeled dataset. This type of patterns is valuable in
various domains such as bioinformatics, data classification. 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 other algorithms. Experiment on real
data also shows that SSDPS efficiently detects multiple SNPs combinations in
genetic data.
