An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO

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...

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Veröffentlicht in:Computational Intelligence and Neuroscience Jg. 2014; H. 2014; S. 181 - 190
Hauptverfasser: Zhang, Jian, Shen, Ling
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Limiteds 01.01.2014
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Schlagworte:
Algorithms
Cluster Analysis
Clustering
Clusters
Datasets as Topic
Fuzzy
Fuzzy Logic
Fuzzy set theory
Fuzzy sets
Gene Expression
Humans
Optimization algorithms
Pattern Recognition, Automated - methods
Product Packaging - statistics & numerical data
Searching
Swarm intelligence
Validity
Wine - classification
Wine - statistics & numerical data
Yeasts - genetics
ISSN:1687-5265, 1687-5273, 1687-5273
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Academic Editor: Daoqiang Zhang
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2014/368628