Generation of interpretable fuzzy granules by a double- clustering technique

Abstract

This paper proposes an approach to derive fuzzy granules from numerical data. Granules are first formed by means of a doubleclustering technique, and then properly fuzzified so as to obtain interpretable granules, in the sense that they can be described by linguistic labels. The double-clustering technique involves two steps. First, information granules are… (More)

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