CNNC: a common nearest neighbour clustering approach for gene expression data

Abstract

We present an effective common nearest neighbour-based clustering technique (CNNC) for finding clusters over gene expression data. CNNC attempts to find all the clusters over gene expression data qualitatively. Our algorithm works by finding clusters using a nearest neighbour-based approach. A regulation-based module for finding sub clusters is also presented here. CNNC was tested on several real-life datasets and the effectiveness is established in terms of well known z-score measure and p-value over several real-life datasets. Using z-score analysis we show that CNNC outperforms other comparable algorithms. The p-value analysis shows that our technique is capable in detecting biologically relevant clusters from gene expression data.