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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Modified Cat Swarm Optimization for Clustering
verfasst von : Saad Razzaq, Fahad Maqbool, Amir Hussain
Erschienen in: Advances in Brain Inspired Cognitive Systems
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Abstract
Clustering is one of the most challenging optimization problems. Many Swarm Intelligence techniques including Ant Colony optimization (ACO), Particle Swarm Optimization (PSO), and Honey Bee Optimization (HBO) have been used to solve clustering. Cat Swarm Optimization (CSO) is one of the newly proposed heuristics in swarm intelligence, which is generated by observing the behavior of cats, and has been used for clustering and numerical function optimization. CSO based clustering is dependent on a pre-specified value of K i.e. Number of Clusters. In this paper we have proposed a “Modified Cat Swam Optimization (MCSO)” heuristic to discover clusters based on the nature of data rather than user specified K. MCSO performs a data scan to determine the initial cluster centers. We have compared the results of MCSO with CSO to demonstrate the enhanced efficiency and accuracy of our proposed technique.