A stochastic nature inspired metaheuristic for clustering analysisA stochastic nature inspired metaheuristic for clustering analysis Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-052008enThis 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.http://creativecommons.org/licenses/by/4.0/International Journal of Business Intelligence and Data Mining1330-44 Marinakis Ioannis Μαρινακης Ιωαννης Matsatsinis Nikolaos Ματσατσινης Νικολαος Marinaki Magdalini Μαρινακη Μαγδαληνη Inderscience Genetic algorithms