Hierarchical Density Shaving: A clustering and visualization framework for large biological datasets

摘要

In many clustering applications for bioinformatics, only part of the data clusters into one or more groups while the rest needs to be pruned. For such situations, we present Hierarchical Density Shaving (HDS), a framework that consists of a fast, hierarchical, density-based clustering algorithm. Our framework also provides a simple yet powerful 2-D visualization of the hierarchy of clusters that can be very useful for further exploration. We present results to show the effectiveness of our methods.