Analysis of Ant Colony Clustering (acc)

Clustering, one of the fundamental tasks in Datamining, is also challenging field of research. Clustering can be considered as an optimization problem as it minimizes inter cluster similarity and maximize intra cluster similarity. Clustering problem has been solved by different algorithms ranging from simple K-means to Bio-inspired Evolutionary algorithms. Swarm intelligence, an emerging Evolutionary computational intelligent technique that has been derived from Bioinspired behavior, has been applied to solve various real time implication problems of combinatorial optimization problem & computational problems. Swarm Intelligent encompasses the implementation of “Collective Intelligence” by groups of simple agents. It is based on the behavior of real world insect Swarms, as a problem solving tool. One such Swarm Behavioral model, Ant colony optimization(ACO) witnesses its application in the field of Datamining & knowledge discovery schemes in recent years. Ant colony optimization method has its ideology taken from the behavior of natural Ants. Ant colony based heuristics are widely studied in the field of computer science and its applications. This paper analyzes the application of Ant colony approach on clustering, by implementing it on some of the predominant data sets and also to compare the method with the existing method. The paper discusses the efficiency of the method based on the results.