ABSTRACT
This chapter reviews different kinds of biological data where clustering has provided promising and biologically meaningful results. It discusses the role of clustering for gene expression data, biological networks, and sequence data. The chapter describes various types of clustering and the corresponding state-of-the-art clustering techniques for each category in the context of microarray data analysis. It provides several protein interaction network clustering algorithms and also describes the state-of-the-art biological sequence clustering algorithms. The chapter explores software packages that implement most of the popular biological clustering algorithms. It explains some of the popular proximity measures used and aims to categorizes the clustering methods proposed in the literature in the context of gene expression data analysis. The chapter reviews the most representative methods that are widely used for analyzing gene expression datasets. It also describes a discussion about biologically validating the results of the clustering methods. Cluster Affinity Search Technique is another graph-based clustering algorithm.
