A New Method for Initialising the K-Means Clustering Algorithm

Abstract

As a classic clustering method, the traditional K-Means algorithm has been widely used in pattern recognition and machine learning. It is known that the performance of the K-means clustering algorithm depend highly on initial cluster centers. Generally initial cluster centers are selected randomly, so the algorithm could not lead to the unique result. In this paper, we present a method to compute initial cluster centers for K-means clustering. Our method is based on an efficient technique for estimating the modes of a distribution. We apply the new method to the K-means algorithm. The experimental results show better performance of the proposed method.