A MST based Uncertain-Partitioning Clustering Algorithm for Gene Expression Data

摘要：

The emerging microarray technology allows scientists simultaneous monitoring of the expression levels of a large number of genes over different time points. Clustering is an important tool for analyzing such microarray data, typical properties of which are its inherent uncertainty, noise and imprecision. In this paper, we propose a MST-based Uncertain-Partitioning (MUP) clustering algorithm, which is a fusion of partition-based clustering and hierarchical clustering in nature. The algorithm identifies iteratively a set of potential inconsistent edges at a time using sliding window approach. By minimizing an objective function of cluster quality, we determine the real inconsistent edges from the potential inconsistent edges, then cut them to form sub clusters. The results of experiments on two real gene expression data sets verify the effectiveness and efficiency of proposed method.

关键词： Gene expression data MST-based clustering Inconsistent edges sliding window.

作者: Xiujun Gong Zhongbo Jiang Hua Yu

作者单位: School of Computer Science and Technology,Tianjin University,Tianjin 300072, China School of Computer Science and Technology,Tianjin University,Tianjin 300072,China

会议类型: 国际会议

会议名称: 2008高等智能国际会议(2008 International Conference on Advanced Intelligence)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-18（万方平台首次上网日期，不代表论文的发表时间）