A Fuzzy k-Modes Algorithm for Clustering Categorical Data

Abstract — 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. Index Terms—Categorical data, clustering, data mining, fuzzy partitioning, k-means algorithm. I