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

• Published in: Computational Intelligence and Neuroscience Vol. 2014; no. 2014; pp. 181 - 190
• Main Authors: Zhang, Jian, Shen, Ling
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2014; Hindawi Publishing Corporation; John Wiley & Sons, Inc
• Subjects: 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
• DOI: 10.1155/2014/368628

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.