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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
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  4:15 p.m., Tuesday, February 27, 2007\\
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  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Werner Stuetzle} \\
  Department of Statistics\\
  University of Washington
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  \subsection*{Generalized Single Linkage Clustering}
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\noindent
Clustering problems occur in may domains, from genomics and astronomy
to document analysis and marketing. The general goal 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. Building on ideas of David Wishart and John Hartigan I will
introduce the cluster tree of a density as a summary statistic
reflecting the group structure, and I will presenent methods for
estimating the cluster tree.

\noindent
This is joint work with Rebecca Nugent (CMU).

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