Davies Bouldin Index based hierarchical initialization K-means

Abstract

K-means algorithm is an effective clustering algorithm based on partition, which has been widely used for clustering analysis. However, there are two main problems for K-means algorithm: how to provide appropriate number of clusters and how to determine initial cluster centers automatically. Plenty of methods have been proposed to address the above problems. In our previous work, we proposed the hierarchical initialization approach to determine initial cluster centers, but we cannot provide the number of clusters automatically. In this paper, in order to determine the number of clusters automatically, we propose the Davies Bouldin Index (DBI) based hierarchical K-means (DHIKM) algorithm on the basis of our previous work. The proposed algorithm can integrate DBI metric into our hierarchical K-means algorithm and can determine the number of clusters with low time cost. Experiments on UCI datasets and synthetic data demonstrate the effectiveness and feasibility of the proposed algorithm.