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A stochastic nature inspired metaheuristic for clustering analysis

Marinakis Ioannis, Matsatsinis Nikolaos, Marinaki Magdalini

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URIhttp://purl.tuc.gr/dl/dias/91C7E257-A537-4E01-8DEC-41A8451D677B-
Identifierhttps://doi.org/ 10.1504/IJBIDM.2008.017974-
Languageen-
Extent15 pagesen
TitleA stochastic nature inspired metaheuristic for clustering analysisen
CreatorMarinakis Ioannisen
CreatorΜαρινακης Ιωαννηςel
CreatorMatsatsinis Nikolaosen
CreatorΜατσατσινης Νικολαοςel
CreatorMarinaki Magdalinien
CreatorΜαρινακη Μαγδαληνηel
PublisherInderscienceen
Content SummaryThis paper presents a new stochastic nature inspired methodology, which is based on the concepts of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), for optimally clustering N objects into K clusters. Due to the nature of stochastic and population-based search, the proposed algorithm can overcome the drawbacks of traditional clustering methods. Its performance is compared with other popular stochastic/metaheuristic methods like genetic algorithm and Tabu search. The proposed algorithm has been implemented and tested on several datasets with very good results.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-05-
Date of Publication2008-
SubjectGenetic algorithmsen
Bibliographic CitationY. Marinakis, M. Marinaki , N. Matsatsinis, " A Stochastic nature inspired metaheuristic for clustering analysis," Inter. J.of Business Intelligence and Data Min., vol.3,no. 1,pp. 30-44, 2008.doi:10.1504/IJBIDM.2008.017974en

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