A New Two-Phase Clustering Algorithm Based on K-means and Hierarchical Clustering with Single-Linkage Agglomerative Method

Abstract

We propose a new clustering algorithm: hierarchical K-means clustering algorithm (HKC), in this paper. HKC consists of two phases. In the first phase, HKC employs K-means clustering algorithm to split the original data into some groups. The purpose of the first phase is to handle the outliers and noises. In the second phase, HKC employs single-linkage agglomerative algorithm, which can discover the arbitrarily shaped clusters and produce a clustering tree, to merge the groups. Since the processed data are simplified to some groups by K-means, the clustering tree could be obtained quickly.In this paper, the accuracy of HKC is evaluated and compared with those of K-means and hierarchical clustering. The experimental results indicated that the accuracy of HKC is better than K-means and hierarchical clustering. Hence HKC could assist the researchers to quickly and accurately analyze data.