Hierarchical Clustering of Gene Ezpression Data with Divergence Measure

摘要：

Hierarchical clustering is a commonly used and valuable approach in clustering analysis.However it depends on the measure used to assess similarity between samples.Two frequently adopted distance measures are Euclidean distance (L2-norm) and city-block distance (L1-norm), and they do not take into account special characteristics of data at hand.In this paper, considering the nonnegativity of gene expression data, we apply a generalized Kullback-Leibler (KL) divergence to measure the similarity in hierarchial clustering analysis.Experimental results on several real cancer related gene expression datasets show that the proposed KL divergence outperforms both L2 and L1 distances.

作者: Weixiang Liu Tianfu Wang Siping Chen Aifa Tang

作者单位: Shenzhen Key Lab of Biomedical Engineering School of Information Engineering Shenzhen University,She Shenzhen Key Lab of Male Reproduction and Genetics Peking University Shenzhen Hospital Shenzhen,5180

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-3

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）