An Improved Initialization Center Algorithm for K-Means Clustering

Abstract

The traditional k-means algorithm has sensitivity to the initial start center. To solve this problem, this paper proposed a new method to find the initial center and improve the sensitivity to the initial centers of k-means algorithm. The algorithm first computes the density of the area where the data object belongs to; then it finds k data objects, which are belong to high density area, as the initial start centers. Experiments based on the standard database UCI show that the proposed method can produce a high purity clustering results and eliminate the sensitivity to the initial centers to some extent.