title: The global Minmaxk-means algorithm.
abstract: The globalk-means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure from suitable initial positions, and employsk-means to minimize the sum of the intra-cluster variances. However the globalk-means algorithm sometimes results singleton clusters and the initial positions sometimes are bad, after a bad initialization, poor local optimal can be easily obtained byk-means algorithm. In this paper, we modified the globalk-means algorithm to eliminate the singleton clusters at first, and then we apply MinMaxk-means clustering error method to globalk-means algorithm to overcome the effect of bad initialization, proposed the global Minmaxk-means algorithm. The proposed clustering method is tested on some popular data sets and compared to thek-means algorithm, the globalk-means algorithm and the MinMaxk-means algorithm. The experiment results show our proposed algorithm outperforms other algorithms mentioned in the paper.
authors.affiliation: School of Information and Communication Engineering, North University of China, Taiyuan, 030051 People's Republic of China.
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Science, North University of China, Taiyuan, 030051 People's Republic of China.
authors.forename: Yanping
authors.initials: Y
authors.lastname: Bai
keywords: Clustering; Global k-means; MinMax k-means; k-Means