关键词: 基因表示数据, 数据聚类, 簇类, 无向图, 连通分支

Abstract: In life sciences，it is necessary to classify the species and genes in order to obtain the knowledge of these species.Using data clustering algorithm can effectively distinguish/identify the mode of gene expression data and categorize them.Those with most similarity are grouped into one category and those with most difference into another category，which is very important to study the structure，function and relations between different genes.Gene expression data in biology science are initially clustered by adopting the method of graph theory and then refined by combining with other method，i.e. k-near neighbor self-learning clustering algorithm or medoid-based self-learning clustering algorithm.Global optimal clusters can be generated for a specific clustering judgment rule.At last analyses and discusses the algorithm，which are tested with simulation data.

Key words: gene expression data, data clustering, cluster, undirected graph, connected components