Abstract-This correspondence describes extensions to the fuzzy k k 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 k k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k k 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 k k-means algorithm.
Fuzzy based k-modes algorithm is proposed in REF .
A fuzzy k-modes algorithm for clustering categorical data
IEEE Trans. Fuzzy Systems
IEEE Trans. Fuzzy Systems
Mathematics Computer Science