XIE Juan-ying1,2, JIANG Shuai1, WANG Chun-xia1, ZHANG Yan1, XIE Wei-xin2
(1 College of Computer Science, Shaanxi Normal University, Xi′an 710062, Shaanxi, China;2 School of Electronic Engineering, Xidian University, Xi′an 710071, Shaanxi, China)
Abstract:
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.
KeyWords:
K-means; global K-means; fast global K-means; K-medoids clustering
