# Statistically Significant Discriminative Patterns Searching

## TL;DR

This paper introduces SSDPS, a novel efficient algorithm for discovering statistically significant discriminative patterns in two-class datasets, outperforming existing methods in speed and pattern reduction, with applications in bioinformatics.

## Contribution

The paper presents SSDPS, a new pattern mining algorithm that exploits anti-monotonicity for efficient discriminative pattern discovery, reducing pattern numbers and improving performance.

## Key findings

- SSDPS outperforms existing algorithms in efficiency.
- SSDPS generates fewer patterns than competitors.
- Effective in detecting SNP combinations in genetic data.

## Abstract

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.