Fuzzy cluster validation index based on inter-cluster proximity
A new cluster validity index is proposed for fuzzy partitions obtained from Fuzzy C-Means algorithm. The proposed validity index exploits an inter-cluster proximity between fuzzy clusters. The inter-cluster proximity is used to measure the degree of overlap between clusters. A low proximity value indicates well-partitioned clusters. The best fuzzy c-partition is obtained by minimizing the inter-cluster proximity with respect to c. Well-known data sets are tested to show the effectiveness and reliability of the proposed index. (C) 2003 Elsevier B.V. All rights reserved.
- Publisher
- ELSEVIER SCIENCE BV
- Issue Date
- 2003-11
- Language
- English
- Article Type
- Article
- Keywords
VALIDITY INDEX; SEGMENTATION; SETS
- Citation
PATTERN RECOGNITION LETTERS, v.24, pp.2561 - 2574
- ISSN
- 0167-8655
- Appears in Collection
- BiS-Journal Papers(저널논문)
- Files in This Item
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