Categorical fuzzy k-modes clustering with automated feature weight learning

This article presents and investigates a new variant of the fuzzy k-Modes clustering algorithm for categorical data with automated feature weight learning. The modification strengthens the classical fuzzy k-Modes algorithm by associating higher weights to features which are instrumental in recognizing the clustering pattern of the data. A statistical comparison between the performances of the proposed algorithm and the conventional fuzzy k-Modes algorithm on synthetic and real world datasets, have been carried out with respect to mean values, best performance count, and medians. We take a novel approach towards the comparison of the fuzziness of the obtained clusters. To the best of our knowledge, such comparison has been reported here for the first time for the case of categorical data. The results obtained, shows that the proposed algorithm enjoys an edge over the conventional fuzzy k-Modes algorithm both in terms of Rand Index and fuzziness measures.

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