Enhanced fuzzy clustering algorithm and cluster validity index for human perception

In this study, we propose an enhanced fuzzy clustering algorithm related to α-cut interval descriptions of fuzzy numbers and a new cluster validity index, which occurs by α-cut intervals and adding two ad hoc functions in the compactness and separability measures. As an application, we use the enhan...

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Bibliographic Details
Published in:Expert systems with applications 2013-02, Vol.40 (3), p.929-937
Main Authors: BASKIR, M. Bahar, TÜRKSEN, I. Burhan
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Cluster validity index
Clustering
Clusters
Computer science
control theory
systems
Data processing. List processing. Character string processing
Design alternative
Exact sciences and technology
Fuzzy
Fuzzy clustering
Fuzzy logic
Fuzzy set theory
Intervals
Memory organisation. Data processing
Software
Suppliers
α-Cut
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Summary:In this study, we propose an enhanced fuzzy clustering algorithm related to α-cut interval descriptions of fuzzy numbers and a new cluster validity index, which occurs by α-cut intervals and adding two ad hoc functions in the compactness and separability measures. As an application, we use the enhanced fuzzy clustering algorithm and its proposed validity index to rank supplier firms of a Turkish Machinery Corporation by design alternatives. In addition, the rankings of supplier firms are determined with a proposed decision measure.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2012.05.049