A repulsive clustering algorithm for gene expression data

Facing the development of microarray technology, clustering is currently a leading technique to gene expression data analysis. In this paper we propose a novel algorithm called repulsive clustering, which is developed for the use of gene expression data analysis. Our performance demonstration on several synthetic and real gene expression data sets show that the repulsive clustering algorithm, compared with some other well-known clustering algorithms, is capable of not only producing even higher quality output, but also easier to implement for immediate use on various situations.