Conditional discriminative pattern mining

Discriminative pattern mining is used to discover a set of significant patterns that occur with disproportionate frequencies in different class-labeled data sets. Although there are many algorithms that have been proposed, the redundancy issue that the discriminative power of many patterns mainly derives from their sub-patterns has not been resolved yet. In this paper, we consider a novel notion dubbed conditional discriminative pattern to address this issue. To mine conditional discriminative patterns, we propose an effective algorithm called CDPM (Conditional Discriminative Patterns Mining) to generate a set of non-redundant discriminative patterns. Experimental results on real data sets demonstrate that CDPM has very good performance on removing redundant patterns that are derived from significant sub-patterns so as to generate a concise set of meaningful discriminative patterns.