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A stochastic nature inspired metaheuristic for clustering analysis

Marinakis Ioannis, Matsatsinis Nikolaos, Marinaki Magdalini

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URI: http://purl.tuc.gr/dl/dias/CF5F6371-5962-430F-9381-10F84BBEB1BB
Year 2007
Type of Item Conference Paper Abstract
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Bibliographic Citation 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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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.

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