Automated Hierarchical Density Shaving and Gene DIVER

A key application of clustering data obtained from sources such as microarray, protein mass spectroscopy, and phylogenetic profile is the detection of functionally related genes. Typically, only a small number of functionally related genes cluster into one or more groups, and the rest need to be pruned. For such situations, we present Automated Hierarchical Density Shaving (Auto-HDS), a framework that consists of a fast, hierarchical, densitybased clustering algorithm and an unsupervised model selection strategy. Auto-HDS can automatically select between clusters of different densities, present them in a compact hierarchy and rank individual clusters using an innovative stability criteria. Our framework also provides a simple yet powerful 2-D visualization of the hierarchy of clusters that can be very useful for further exploring the dense clusters in high-dimensional datasets. We present results on Gasch and Lee microarray datasets to show the effectiveness of our methods.