文章基本信息

标题：Comparative Study of Particle Swarm Optimization based Unsupervised Clustering Techniques
作者：V.K.Panchal ; Harish Kundra ; Jagdeep Kaur 等
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2009
卷号：9
期号：10
页码：132-140
出版社：International Journal of Computer Science and Network Security
摘要：In order to overcome the shortcomings of traditional clustering algorithms such as local optima and sensitivity to initialization, a new Optimization technique, Particle Swarm Optimization is used in association with Unsupervised Clustering techniques in this paper. This new algorithm uses the capacity of global search in PSO algorithm and solves the problems associated with traditional clustering techniques. This merge avoids the local optima problem and increases the convergence speed. Parameters, time, distance and mean, are used to compare PSO based Fuzzy C-Means, PSO based Gustafson��s-Kessel, PSO based Fuzzy K-Means with extragrades and PSO based K-Means are suitably plotted. Thus, Performance evaluation of Particle Swarm Optimization based Clustering techniques is achieved. Results of this PSO based clustering algorithm is used for remote image classification. Finally, accuracy of this image is computed along with its Kappa Coefficient.
关键词：Particle Swarm Optimization(PSO); Fuzzy C-Means Clustering (FCM); K-Means Clustering (K-Means); Swarm Clustering; Gustaffsons-Kessel Clustering (GK); Unsupervised Clustering; Remote Sensing; Image Clustering; Image Classification