title: An improved fuzzy c-means clustering algorithm based on shadowed sets and PSO.
abstract: 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.
authors.affiliation: School of Mechanical Engineering, Tongji University, Shanghai 200092, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Precision Medical Device Department, University of Shanghai for Science and Technology, Shanghai 200093, China.
authors.forename: Ling
authors.initials: L
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D066264:Datasets as Topic; D017143:Fuzzy Logic; D015870:Gene Expression; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D019064:Product Packaging / Q000706:statistics & numerical data; D014920:Wine / Q000145:classification / Q000706:statistics & numerical data; D015003:Yeasts / Q000235:genetics