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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/CF5F6371-5962-430F-9381-10F84BBEB1BB-
Languageen-
TitleA stochastic nature inspired metaheuristic for clustering analysisen
CreatorMarinakis Ioannisen
CreatorΜαρινακης Ιωαννηςel
CreatorMatsatsinis Nikolaosen
CreatorΜατσατσινης Νικολαοςel
CreatorMarinaki Magdalinien
CreatorΜαρινακη Μαγδαληνηel
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 ItemΠερίληψη Δημοσίευσης σε Συνέδριοel
Type of ItemConference Paper Abstracten
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-06-
Date of Publication2007-
SubjectGAs (Algorithms)en
SubjectGenetic searches (Algorithms)en
Subjectgenetic algorithmsen
Subjectgas algorithmsen
Subjectgenetic searches algorithmsen
Bibliographic CitationY. Marinakis, M. Marinaki, N. Matsatsinis,"A stochastic nature inspired metaheuristic for clustering analysis, "in 22nd European Conference on Operational Research ,2007.en

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