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A fuzzy k-modes algorithm for clustering categorical data

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2 Author(s)
Zhexue Huang ; Manage. Inf. Principles Ltd., Melbourne, Vic., Australia ; Ng, M.K.

This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of the k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k-modes algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:7 ,  Issue: 4 )