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Building granules in asymmetric relational data is still a challenging problem. In this paper, we present an approach that transcribes asymmetric property in a proximity matrix into a set of binary classifications constituted with respect to the directional proximity from each object. Indiscernibility of objects are then assessed based on the Jaccard coefficient that quantifies class commonality of...
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