Title: A stochastic nature inspired metaheuristic for clustering analysis

Authors: Yannis Marinakis, Magdalene Marinaki, Nikolaos Matsatsinis

Addresses: Department of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece. ' Department of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece. ' Department of Production Engineering and Management, Technical University of Crete, 73100 Chania, Greece

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.

Keywords: nature inspired intelligence; clustering analysis; feature selection problem; FSP; particle swarm optimization; PSO; ant colony optimization; ACO; metaheuristics; genetic algorithms; Tabu search.

DOI: 10.1504/IJBIDM.2008.017974

International Journal of Business Intelligence and Data Mining, 2008 Vol.3 No.1, pp.30 - 44

Published online: 25 Apr 2008 *

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