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

Authors:
Gunjan Gupta;Alexander Liu;Joydeep Ghosh
Affiliations:
University of Texas at Austin;University of Texas at Austin;University of Texas at Austin
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

Citing 0
Cited 2

Automated Hierarchical Density Shaving: A Robust Automated Clustering and Visualization Framework for Large Biological Data Sets

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

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Abstract

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.