DocumentCode
1545416
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
Volume
7
Issue
4
fYear
1999
fDate
8/1/1999 12:00:00 AM
Firstpage
446
Lastpage
452
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;
fLanguage
English
Journal_Title
Fuzzy Systems, IEEE Transactions on
Publisher
ieee
ISSN
1063-6706
Type
jour
DOI
10.1109/91.784206
Filename
784206
Link To Document