Tutorial at the First SIAM International Conference on Data Mining
                             Chicago 
                          April 5-7, 2001


           Data Mining for Science and Engineering Applications

                            Chandrika Kamath
                 Center for Applied Scientific Computing
                 Lawrence Livermore National Laboratory


Data analysis techniques have long been used to analyze scientific and
engineering data.  With sensors becoming ubiquitous, computers simulating
complex processes at an unprecedented pace, and ever-improving data storage
capabilities, petabyte-scale datasets are becoming routine.  This has led
to the innovative application of data mining techniques to novel and
challenging problems. In this tutorial, we will first give a brief
introduction to data mining in the context of scientific and engineering
applications. Using examples from diverse fields such as astronomy,
biology, physics, and remote sensing, we will identify the common threads
that permeate the mining of scientific/engineering data.  We will also
illustrate how issues such as feature extraction and data fusion differentiate
scientific data mining from its commercial counterpart.  Our goal is to
show that the diversity of applications, the richness of the problems faced
by practitioners, and the opportunity to borrow ideas from other more
established areas of data analysis, make scientific data mining an exciting
and challenging field.


Chandrika Kamath received the Ph.D. degree in computer science from the
University of Illinois at Urbana-Champaign in 1986. Prior to joining
Lawrence Livermore National Laboratory in 1997, Chandrika was a Consulting
Software Engineer at Digital Equipment Corporation.  Her research interests
are in large-scale data mining and pattern recognition, including image
processing, feature extraction, dimension reduction, and classification and
clustering algorithms. She is also interested in the practical application
of these techniques.  Chandrika is currently the project lead and an
individual contributor for Sapphire, a project in large-scale data mining.


UCRL-JC-142626: This work was performed under the auspices of the
U.S. Department of Energy by University of California Lawrence Livermore
National Laboratory under contract No. W-7405-Eng-48.