An improved ant-based clustering algorithm

Abstract

Clustering is a popular data analysis and data mining technique. In this paper, an improved ant colony clustering algorithm is presented to optimally partition N objects into K clusters and a comparative study has been made to prove its high performance using four evaluation measures. This algorithm has been tested on several synthetic datasets compared with the proposed ant colony based clustering algorithm called ACA. The experimental data reveals very encouraging results in terms of the quality of clustering.