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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 ( C onditional D iscriminative P atterns M ining) 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. [ABSTRACT FROM AUTHOR]

Association rule mining is an important task in the field of data mining, and many efficient algorithms have been proposed to address this problem. However, a large portion of the rules reported by these algorithms just satisfy the user-defined constraints purely by accident, and those that are not statistically meaningful should be filtered out through statistical significance testing. In the context of association rule discovery, the permutation-based approach can achieve better performance than other competitive methods, although several drawbacks of this effective approach narrow its usability. In this paper, we provide an analysis of these disadvantages and propose an algorithm called Exact Permutation p -values for Association Rules (EPAR) to calculate the exact p -values of all tested rules. Experiments on different types of data sets demonstrate that EPAR can successfully alleviate the disadvantages and outperform the direct permutation-based method over several performance measures. [ABSTRACT FROM AUTHOR]