Sample index based encoding for clustering using evolutionary computation

Abstract

Clustering is a commonly used unsupervised machine learning method, which automatically organized data into different clusters according to their similarities. In this paper, we carried out a throughout research on evolutionary computation based clustering. This paper proposed a sample index based encoding method, which significantly reduces the search space of evolutionary computation so the clustering algorithm converged quickly. Evolutionary computation has a good global search ability while the traditional clustering method k-means has a better capability at local search. In order to combine the strengths of both, this paper researched on the effect of initializing k-means by evolutionary computation algorithms. Experiments were conducted on five commonly used evolutionary computation algorithms. Experimental results show that the sample index based encoding method and evolutionary computation initialized k-means both perform well and demonstrate great potential.