A New Clustering Method Based on Artificial Fish-Swarm Algorithm
Xue Hui-feng · Jisuanji fangzhen · 2009
Clustering analysis is a division of data into similarity groups according to given rules. Traditional clustering algorithms generally have some problems,such as the sensitivity to initializing parameter,difficulty of finding out the optimized clustering result and the validity of clustering. Artificial fish-swarm algorithm(AFSA) as a novel bio-inspired optimization method possesses good capability to avoid the local extreme and obtain the global extreme.This paper introduces a mathematical model of clusters. A new clustering algorithm based on artificial fish-swarm algorithm was proposed which combined artificial fish-swarm algorithm with clustering theory based on the fundamental principle of two methods. Compared with ant colony algorithm,a well-known data mining algorithm for clustering,this method is proved to be better for clustering.