An improved global K-means clustering algorithm
Xie Wei-xin · Journal of Shaanxi Normal University · 2010
An improved global K-means clustering algorithm is proposed by presenting a novel method of generating the next optimal initial center with the enlightening of the idea of K-medoids clustering algorithm suggested by Park et al.Our new method choose a point which has a high density and is far away from the centers of the available clusters,so that it can not only avoid choosing a noisy datum as the optimal candidate centre,but also reduce the computational time without affecting the performance of the global K-means clustering algorithm.Our improved global K-means clustering algorithm is tested on some well-known data sets from UCI and on some synthetic data with noisy data,and the results of these experiments demonstrate that our method significantly outperforms the global K-means clustering algorithm and the fast global K-means clustering algorithm.