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Fuzzy clustering of categorical data using fuzzy centroids
- Authors
- Kim, Dae-Won; Lee, K.H.; Lee, D.
- Issue Date
- Aug-2004
- Publisher
- ELSEVIER SCIENCE BV
- Keywords
- fuzzy clustering; k-modes algorithm; fuzzy k-modes algorithm; categorical data; fuzzy centroid
- Citation
- PATTERN RECOGNITION LETTERS, v.25, no.11, pp 1263 - 1271
- Pages
- 9
- Journal Title
- PATTERN RECOGNITION LETTERS
- Volume
- 25
- Number
- 11
- Start Page
- 1263
- End Page
- 1271
- ISSN
- 0167-8655
1872-7344
- Abstract
- In this paper the conventional fuzzy k-modes algorithm for clustering categorical data is extended by representing the clusters of categorical data with fuzzy centroids instead of the hard-type centroids used in the original algorithm. Use of fuzzy centroids makes it possible to fully exploit the power of fuzzy sets in representing the uncertainty in the classification of categorical data. To test the proposed approach, the proposed algorithm and two conventional algorithms (the k-modes and fuzzy k-modes algorithms) were used to cluster three categorical data sets. The proposed method was found to give markedly better clustering results. (C) 2004 Elsevier B.V. All rights reserved.
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