文章基本信息

标题：COOPERATIVE SWARM INTELLIGENCE BASED EVOLUTIONARY APPROACH TO FIND OPTIMAL CLUSTER CENTER IN CLUSTER ANALYSIS
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作者：BIGHNARAJ NAIK ; SARITA MAHAPATRA
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2012
卷号：42
期号：1
页码：008-017
出版社：Journal of Theoretical and Applied
摘要：The Centroid-based clustering is an NP-hard optimization problem and the common approach is to search for cluster centers only for approximate solutions. In this paper we have proposed swarm intelligence based nature-inspired center-based clustering method using PSO optimization. Proposed PSO clustering method is capable to search best cluster with maximum fitness using social-only and cognition-only model, such that the square distances from the cluster are minimized. In this article, how PSO based clustering can be used to get N number of cluster specified by the user in a dataset is demonstrated. Our suggested method has been tested with artificial dataset and several datasets from UCI Machine learning repository. Effectiveness and usefulness of the proposed method is shown by comparing fitness of this method with K-means and Fuzzy c-means technique. For better comparative result, we have ended up with comparison of proposed clustering model with subtractive clustering (extension of the mountain clustering) to ensure proposed method computes optimal number of cluster in a dataset. Results shows that, this method is quite simple, effective and has much potential to search best cluster centers in multidimensional search space.
关键词：Swarm intelligence (SI); Particle swarm optimization (PSO); Centroid-based clustering; Cluster Analysis; Fuzzy C-means clustering (FCM); K-Means clustering; Subtractive Clustering; Euclidean distance