Particle swarm optimization based fuzzy clustering approach to identify optimal number of clusters

• Autorzy: Chen M. , Ludwig S. A.

Identyfikatory

DOI

10.2478/jaiscr-2014-0024

• Języki publikacji: EN

Abstrakty

EN

Fuzzy clustering is a popular unsupervised learning method that is used in cluster analysis. Fuzzy clustering allows a data point to belong to two or more clusters. Fuzzy c-means is the most well-known method that is applied to cluster analysis, however, the shortcoming is that the number of clusters need to be predefined. This paper proposes a clustering approach based on Particle Swarm Optimization (PSO). This PSO approach determines the optimal number of clusters automatically with the help of a threshold vector. The algorithm first randomly partitions the data set within a preset number of clusters, and then uses a reconstruction criterion to evaluate the performance of the clustering results. The experiments conducted demonstrate that the proposed algorithm automatically finds the optimal number of clusters. Furthermore, to visualize the results principal component analysis projection, conventional Sammon mapping, and fuzzy Sammon mapping were used.

Słowa kluczowe

EN

optimization; fuzzy clustering; cluster analysis; particle swarm optimization (PSO); PSO; fuzzy Sammon mapping; Sammon mapping

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2014
• Tom: Vol. 4, No. 1
• Strony: 43--56
• Opis fizyczny: Bibliogr. 47 poz., rys.

Twórcy

autor

Chen M.

• Department of Computer Science, North Dakota State University Fargo, ND, USA

autor

Ludwig S. A.

• Department of Computer Science, North Dakota State University Fargo, ND, USA

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