![Institutional Repository [SANDBOX]](images/dias.logo.png "Institutional Repository [SANDBOX]"){kind=logo}
Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/91C7E257-A537-4E01-8DEC-41A8451D677B | - |
| Identifier | https://doi.org/ 10.1504/IJBIDM.2008.017974 | - |
| Language | en | - |
| Extent | 15 pages | en |
| Title | A stochastic nature inspired metaheuristic for clustering analysis | en |
| Creator | Marinakis Ioannis | en |
| Creator | Μαρινακης Ιωαννης | el |
| Creator | Matsatsinis Nikolaos | en |
| Creator | Ματσατσινης Νικολαος | el |
| Creator | Marinaki Magdalini | en |
| Creator | Μαρινακη Μαγδαληνη | el |
| Publisher | Inderscience | en |
| Content Summary | This 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 Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-11-05 | - |
| Date of Publication | 2008 | - |
| Subject | Genetic algorithms | en |
| Bibliographic Citation | Y. 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.2008.017974 | en |
Copyright © DIAS 2013
