Applying clustering and association rule mining for analyzing gene expression data
Xufa Wang · Beijing shengwu yixue gongcheng · 2008
With general application of DNA microarray technology,huge gene expression data are produced.Identifying the regulation relationship among genes from gene expression data is an important research topic in the bioinformatics field.Association rule mining is such a popular technology in data mining field.Yet when directly applying such method to gene expression data,there exist two problems,one is the excessive cost of the time and space,and the other is that the rules obtained only show relationships among genes roughly and can't give any clues about the strength of such relationships.In this paper,an analyzing method is presented.Firstly Clustering is used to reduce the dimension of data,then discretizing the gene expression data using seven intervals that can represent each gene with different magnitudes of expression change,at last mining association rules from the gene expression data.The experiment results show that such analyzing method is effective.