|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||Modified cat swarm optimization for clustering|
|Citation:||Razzaq S, Maqbool F & Hussain A (2016) Modified cat swarm optimization for clustering. In: Liu C, Hussain A, Luo B, Tan K, Zeng Y & Zhang Z (eds.) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science, 10023. BICS 2016: 8th International Conference on Brain-Inspired Cognitive Systems, Beijing, China, 28.11.2016-30.11.2016. Cham, Switzerland: Springer, pp. 161-170. https://link.springer.com/chapter/10.1007/978-3-319-49685-6_15; https://doi.org/10.1007/978-3-319-49685-6_15|
|Series/Report no.:||Lecture Notes in Computer Science, 10023|
|Conference Name:||BICS 2016: 8th International Conference on Brain-Inspired Cognitive Systems|
|Conference Dates:||2016-11-28 - 2016-11-30|
|Conference Location:||Beijing, China|
|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.|
|Status:||VoR - Version of Record|
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|Razzaq_etal_LNCS_2016.pdf||Fulltext - Published Version||2.71 MB||Adobe PDF||Under Embargo until 3000-10-14 Request a copy|
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