Abstract
Contrast set mining has developed as a data mining task which aims at discerning differences amongst groups. These groups can be patients, organizations, molecules, and even time-lines, and are defined by a selected property that distinguishes one from the other. A contrast set is a conjunction of attribute-value pairs that differ significantly in their distribution across groups. The search for contrast sets can be prohibitively expensive on relatively large datasets because every combination of attribute-values must be examined, causing a potential exponential growth of the search space. In this paper, we introduce the notion of a correlated group difference (CGD) and propose a contrast set mining technique that utilizes mutual information and all confidence to select the attribute-value pairs that are most highly correlated, in order to mine CGDs. Our experiments on real datasets demonstrate the efficiency of our approach and the interestingness of the CGDs discovered.
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Simeon, M., Hilderman, R. (2011). GENCCS: A Correlated Group Difference Approach to Contrast Set Mining. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_11
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DOI: https://doi.org/10.1007/978-3-642-23199-5_11
Publisher Name: Springer, Berlin, Heidelberg
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