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A stochastic nature inspired metaheuristic for clustering analysis

Marinakis Ioannis, Matsatsinis Nikolaos, Marinaki Magdalini

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/91C7E257-A537-4E01-8DEC-41A8451D677B
Έτος 2008
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά 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 https://doi.org/ 10.1504/IJBIDM.2008.017974
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Περίληψη

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.

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