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An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-11-11
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering.
SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries.
This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters.
Experiments show that the proposed approach significantly improves the clustering effect.
American Psychological Association (APA)
Zhang, Jian& Shen, Ling. 2014. An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1016724
Modern Language Association (MLA)
Zhang, Jian& Shen, Ling. An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1016724
American Medical Association (AMA)
Zhang, Jian& Shen, Ling. An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1016724
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1016724