Gene expression data clustering analysis: A survey

Abstract

The advent of DNA microarray technology has enabled biologists to monitor the expression levels (MRNA) of thousands of genes simultaneously. In this survey, we address various approaches to gene expression data analysis using clustering techniques. We discuss the performance of various existing clustering algorithms under each of these approaches. Proximity measure plays an important role in making a clustering technique effective. Therefore, we briefly discuss various proximity measures. Finally, since evaluation of the effectiveness of the clustering techniques over gene data requires validity measures and data sources for numeric data, we discuss them as well.