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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/CF5F6371-5962-430F-9381-10F84BBEB1BB
Έτος 2007
Τύπος Περίληψη Δημοσίευσης σε Συνέδριο
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά Y. Marinakis, M. Marinaki, N. Matsatsinis,"A stochastic nature inspired metaheuristic for clustering analysis, "in 22nd European Conference on Operational Research ,2007.
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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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