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David Scott
is the Noah Harding Professor of Statistics
at Rice University in Houston. He was a founding member of the
Department of Statistics in 1987, and chairman from 1990-1993. He
was editor of the Journal of Computational and Graphical
Statistics from 2000-2004. From 2000-2005, he served as a member
of the National Research Council's Committee on Applied and
Theoretical Statistics. As part of that activity, he organized a
meeting on real-time data mining, held in December, 2002. He is
also a member of the John Wiley Editorial Board on Probability and
Statistics. He is past Editor of Computational Statistics, and
past Associate Editor of the AOS, JASA, and Statistical Sciences.
He is a Fellow of the American
Statistical Association, the Institute of Mathematical Statistics,
and the American Association for the Advancement of Science. He is
also an elected member of the International Statistics Institute.
He was named the Texas Statistician of the Year in 1993 and
received the U.S. Army Wilks Award in 2004.
His research
interests include multivariate data analysis, nonparametric
function estimation, clustering, data mining, robust estimation,
outlier detection, and statistical computing. He is author of the
1992 Wiley book ``Multivariate Density Estimation: Theory,
Practice, and Visualization'' as well as numerous scientific
papers.
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Mixtures and Kernels
for Density-Based Clustering
Mixture modeling provides an effective
framework for complex, high-dimensional data. The estimated components
provide natural clusters. Likewise, kernel densities may be used to
define clusters through modal information. The advantages and
disadvantages of each are examined, and research opportunities
explored.
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