COSINE: a vertical group difference approach to contrast set mining
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
GENCCS: a correlated group difference approach to contrast set mining
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Classification of type-2 diabetic patients by using Apriori and predictive Apriori
International Journal of Computational Vision and Robotics
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Contrast sets have been shown to be a useful tool for describing differences between groups. A contrast set is a set of association rules for which the antecedents de- scribe distinct groups, a common consequent is shared by all the rules, and support for the rules is significantly dif- ferent between groups. While techniques for generating contrast sets containing categorical attributes in the con- sequent are "straightforward", techniques for generating contrast sets containing continuous-valued attributes are not. In this paper, we describe a technique for generat- ing contrast sets describing the differences between two groups, where the consequent in the rules contains up to two continuous-valued attributes. We propose a modified equal- width binning interval approach to discretizing continuous- valued attributes, where the approximate width of the de- sired intervals is provided as a parameter to the model. We also propose an objective measure for identifying and rank- ing the potentially interesting contrast sets. Experimental results demonstrate the effectiveness of our approach and the utility of the interest measure.