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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, October 2, 2001}
\centerline{Sequoia Hall Rm. 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Jerome H. Friedman}
\centerline{\sl Department of Statistics}
\centerline{\sl Stanford University}
\centerline{\sl Stanford,  CA 94305}
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\centerline{\bf Weighted Harmonic Distance Clustering}

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A dissimilarity measure for value-attribute data is proposed for use
in cluster analysis. It assigns small dissimilarities to observation
pairs that have close values on any subset of the attribute variables
regardless of their values on the complement set of variables. Using
this measure in conjunction with dissimilarity based clustering
algorithms encourages the detection of subgroups of observations that
preferentially cluster on subsets of the variables. The relevant
variable subsets for each individual cluster can be different and
partially (or completely) overlap with those of other
clusters. Enhancements for increasing sensitivity for detecting
especially low cardinality groups clustering on a small subset of
variables are discussed. Applications in several different domains,
including gene expression arrays, are presented.

Joint work with Jacqueline Meulman, Leiden University.
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