Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26255
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dc.contributor.authorRazzaq, Saaden_UK
dc.contributor.authorMaqbool, Fahaden_UK
dc.contributor.authorHussain, Amiren_UK
dc.contributor.editorLiu, CLen_UK
dc.contributor.editorHussain, Aen_UK
dc.contributor.editorLuo, Ben_UK
dc.contributor.editorTan, KCen_UK
dc.contributor.editorZeng, Yen_UK
dc.contributor.editorZhang, Zen_UK
dc.date.accessioned2017-12-01T00:41:45Z-
dc.date.available2017-12-01T00:41:45Z-
dc.date.issued2016en_UK
dc.identifier.urihttp://hdl.handle.net/1893/26255-
dc.description.abstractClustering 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.en_UK
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.relationRazzaq 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://doi.org/10.1007/978-3-319-49685-6_15en_UK
dc.relation.ispartofseriesLecture Notes in Computer Science, 10023en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectClusteringen_UK
dc.subjectCat Swarm Optimizationen_UK
dc.subjectSwarm Intelligenceen_UK
dc.titleModified cat swarm optimization for clusteringen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate3000-10-14en_UK
dc.rights.embargoreason[Razzaq_etal_LNCS_2016.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1007/978-3-319-49685-6_15en_UK
dc.citation.issn0302-9743en_UK
dc.citation.spage161en_UK
dc.citation.epage170en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailahu@cs.stir.ac.uken_UK
dc.citation.btitleAdvances in Brain Inspired Cognitive Systems. BICS 2016en_UK
dc.citation.conferencedates2016-11-28 - 2016-11-30en_UK
dc.citation.conferencelocationBeijing, Chinaen_UK
dc.citation.conferencenameBICS 2016: 8th International Conference on Brain-Inspired Cognitive Systemsen_UK
dc.citation.date13/11/2016en_UK
dc.citation.isbn978-3-319-49684-9en_UK
dc.citation.isbn978-3-319-49685-6en_UK
dc.publisher.addressCham, Switzerlanden_UK
dc.contributor.affiliationUniversity of Sargodhaen_UK
dc.contributor.affiliationUniversity of Sargodhaen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.scopusid2-s2.0-84997419174en_UK
dc.identifier.wtid538452en_UK
dc.contributor.orcid0000-0002-8080-082Xen_UK
dc.date.accepted2016-08-10en_UK
dc.date.filedepositdate2017-11-30en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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