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Discriminative pattern mining is one of the most important techniques in data mining. This challenging task is concerned with finding a set of patterns that occur with disproportionate frequency in data sets with various class labels. Such patterns are of great value for group difference detection and classifier construction. Research on finding interesting(More)
Affinity purification-mass spectrometry (AP-MS) has been widely used for generating bait-prey data sets so as to identify underlying protein-protein interactions and protein complexes. However, the AP-MS data sets in terms of bait-prey pairs are highly noisy, where candidate pairs contain many false positives. Recently, numerous computational methods have(More)
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(More)
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