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Nonparametric cluster analysis: estimating the cluster tree of a density

Presented by: 
W Stuetzle [Washington]
Date: 
Wednesday 9th January 2008 - 11:30 to 12:30
Venue: 
INI Seminar Room 1
Session Chair: 
Jianqing Fan
Abstract: 

The general goal of clustering is to identify distinct groups in a collection of objects. To cast clustering as a statistical problem we regard the feature vectors characterizing the objects as a sample from some unknown probability density. The premise of nonparametric clustering is that groups correspond to modes of this density. The cluster tree summarizes the connectivity structure of the level sets of a density; leaves of the tree correspond to modes of the density. I will define the cluster tree, present methods for its estimating, show examples, and discuss some open problems.

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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons