Gene Ezpression Data Cluster Analysis

摘要：

The explosive growth of the gene expression data needs an automatic and effective data analysis tool urgently. Presently, clustering has become the powerful and widely used method in gene expression data analysis to obtain biological information. However, there are problems in analyzing gene expression data of over-dependence on the distribution of dataset and impossibly achieving a global optimal clustering effect. This paper introduces the spectral clustering method. The advantage of this method is that it can be used in any shape of sample space and converge in the global optimal. In experiment, We use yeast cell cycle and Lyers serum data set as the test data set and select adjust-FOM as the evaluation criteria. The result shows the spectral clustering method in the clustering effect is better than traditional clustering methods.

关键词： gene ezpression data clustering technology spectral clustering

作者: Ping Guo Xiao-yan Deng

作者单位: School of Computer Science Chongqing University Chongqing, 400044, China

会议类型: 国际会议

会议名称: 2009 WASE International Conference on Information Engineering(2009年国际信息工程会议)(ICIE 2009)

会议地点: 太原

会议语种:英文

页码: 99-102

在线出版日期: 2009-07-10（万方平台首次上网日期，不代表论文的发表时间）