Clustering Gene Expression Data Based on Minimization of Within-Class Covariance Matrix

Abstract

DNA microarray technology has facilitated in obtaining massive gene expression data. Efficient categorizing methods are necessary to analyze such massive data. There are several clustering methods to categorize the gene expression data into functionally meaningful groups. One method is a Self-Organizing Map ({SOM}) that presents high-dimensional data by low-dimensional data. However, it is difficult to find clustering boundaries from results of the SOM. To understand how the {SOM} categorizes the gene expression data, we apply a method of combining the {SOM} and a clustering method based on minimization of within-class covariance matrix. We show that the proposed method is effective for categorizing the published data of yeast gene expression.