Clustering Algorithm with Ant Colony Based on Stochastic Best Solution Kept

Ant colony optimization (ACO) is a population-based meta-heuristic that can be used to find approximate solutions to difficult optimization problems. Clustering Analysis, which is an important method in data mining, classifies a set of observations into two or more mutually exclusive unknown groups. This paper presents a novel clustering algorithm with ant colony based on stochastic best solution kept--ESacc. The algorithm is based on Sacc that was proposed by P.S.Shelokar and presents a method that best values are kept stochastically. The results of several times experiments in three datasets show that the new algorithm-ESacc is less in running time, is better in clustering effect and more stable than Sacc. Experimental results validate the novel algorithm’s efficiency.