CL.E.KMODES: a modified $k$-modes clustering algorithm In this paper we present a new method for clustering categorical data sets named CL.E.KMODES. The proposed method is a modified $k$-modes algorithm that incorporates a new four-step dissimilarity measure, which is based on elements of the methodological framework of the ELECTRE I multicriteria method. The four-step dissimilarity measure introduces an alternative and more accurate way of assigning objects to clusters. In particular, it compares each object with each mode, for every attribute that they have in common, and then chooses the most appropriate mode and its corresponding cluster for that object. Seven widely used data sets are tested to verify the robustness of the proposed method in six clustering evaluation measures.

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  1. Mastrogiannis, N.; Giannikos, I.; Boutsinas, B.; Antzoulatos, G.: CL.E.KMODES: a modified $k$-modes clustering algorithm (2009)