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Hierarchical Density Shaving: A clustering and visualization framework for large biological datasets
Hong Kong, China December 18-December 22
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.92Sixth IEEE International Conference o ...
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Gunjan Gupta, University of Texas at Austin
Alexander Liu, University of Texas at Austin
Joydeep Ghosh, University of Texas at Austin
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.
Citation:
Gunjan Gupta, Alexander Liu, Joydeep Ghosh, "Hierarchical Density Shaving: A clustering and visualization framework for large biological datasets," icdmw, pp.89-93, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006
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