A stochastic nature inspired metaheuristic for clustering analysis
by Yannis Marinakis, Magdalene Marinaki, Nikolaos Matsatsinis
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 3, No. 1, 2008

Abstract: 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.

Online publication date: Fri, 25-Apr-2008

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