An Effective Density-Based Hierarchical Clustering Technique to Identify Coherent Patterns from Gene Expression Data

Abstract

We present an effective tree-based clustering technique (Gene ClusTree) for finding clusters over gene expression data. GeneClusTree attempts to find all the clusters over subspaces using a tree-based density approach by scanning the whole database in minimum possible scans and is free from the restrictions of using a normal proximity measure [1]. Effectiveness of GeneClusTree is established in terms of well known z-score measure and p-value over several real-life datasets. The p-value analysis shows that our technique is capable in detecting biologically relevant clusters from gene expression data.