An Improved Cat Swarm Optimization Algorithm for Clustering

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)

Abstract

Clustering is an efficient technique that can be put in place to find out some sort of relationship in the data. Large number of heuristic approaches have been used for clustering task. The Cat Swarm Optimization (CSO) is the latest meta-heuristic algorithm which has been applied in clustering field and provided better results than K-Means and Particle Swarm Optimization (PSO). However, this algorithm is suffered with diversity problem. To overcome this problem, an improved version of CSO method using Cauchy mutation operator is proposed. The performance of improved CSO is compared with the existing methods like K-Means, PSO and CSO on several artificial and real datasets. From the simulation study, it came to revelation that the improved CSO algorithm gives better quality solution than others.

Keywords

Cat swarm optimization Cauchy mutation operator Clustering and particle swarm optimization 

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringBirla Institute of TechnologyMesra, RanchiIndia

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