Title :
A fuzzy k-modes algorithm for clustering categorical data
Author :
Huang, Zhexue ; Ng, Michael K.
Author_Institution :
Manage. Inf. Principles Ltd., Melbourne, Vic., Australia
fDate :
8/1/1999 12:00:00 AM
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
Keywords :
data mining; fuzzy set theory; pattern clustering; categorical data clustering; fuzzy k-modes algorithm; matching dissimilarity measure; Australia; Clustering algorithms; Clustering methods; Computational complexity; Computational efficiency; Data mining; Databases; Helium; Information management; Partitioning algorithms;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
