The Research of Building Fuzzy C-Means Clustering Model Based on Particle Swarm Optimization

Abstract

Particle Swarm Optimization algorithm is based on iterative optimization tools, system initialization of a group of random solutions, through iterative search for the optimal value. The basic idea of the fuzzy C-means clustering algorithm is to determine each sample data belonging to a certain degree of clustering, and the degree of membership of sample data is grouped into a cluster. Favor optimal solution in the sense of multi-objective particle swarm algorithm is efficient search capabilities. The paper presents the research of Building Fuzzy C-Means Clustering Model Based on Particle Swarm Optimization. Fuzzy c-means clustering is determined membership to each data point belongs to a cluster of a clustering algorithm. Particle Swarm Optimization is the process of the simulated social animals foraging moving group activities work of individual and group coordination and cooperation.  DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3680