RAND Statistics Seminar Series
Visualizing Hierarchical Cluster Structure via Density-Based Linkage Methods and Component Trees
Presented by Rebecca Nugent, Ph.D., Carnegie Mellon University
Friday, November 21, 2008
1:30 p.m. – 3:00 p.m. EST
Conference Room 6202, RAND Corporation, Pittsburgh, PA
Washington, D.C., Conf. Rm. 4132 1:30 p.m. EST
Santa Monica, CA, Conf. Rm. 2309 10:30 a.m. PST
Please contact Denise Miller if you would like to attend this seminar.
The goal of clustering is to identify distinct groups in a population and assign a group label to each observation. To cast clustering as a statistical problem, we regard the data as a sample from an unknown density p(x). To generate clusters, we estimate the properties of p(x) with either parametric (model-based) or nonparametric methods. In model-based clustering, we assume that groups in the population correspond to mixture components in the density estimate; in nonparametric clustering, they correspond to the density estimate's modes. In contrast, the algorithmic approach to clustering (linkage methods, spectral clustering) applies an algorithm, often based on a distance measure to data in m-dimensional space. Clusters are extracted heuristically. We propose to combine the strengths of the different clustering approaches to visualize the (possibly hierarchical) cluster structure.
Rebecca Nugent received her M.S. in Statistics from Stanford University and her PhD in Statistics from the University of Washington. She is currently a Visiting Assistant Professor in the Department of Statistics at Carnegie Mellon University. She primarily works in clustering methodology and visualization.
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