Efficient mining of contrast patterns on large scale imbalanced real-life data

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Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7818 LNAI (PART 1), pp. 62 - 73
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Contrast pattern mining has been studied intensively for its strong discriminative capability. However, the state-of-the-art methods rarely consider the class imbalanced problem, which has been proved to be a big challenge in mining large scale data. This paper introduces a novel pattern, i.e. converging pattern, which refers to the itemsets whose supports contrast sharply from the minority class to the majority one. A novel algorithm, ConvergMiner, which adopts T*-tree and branch bound pruning strategies to mine converging patterns efficiently, is proposed. Substantial experiments in online banking fraud detection show that the ConvergMiner greatly outperforms the existing cost-sensitive classification methods in terms of predicative accuracy. In particular, the efficiency improves with the increase of data imbalance. © Springer-Verlag 2013.
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