This tutorial presents a comprehensive picture of current research
paradigms in the field of KDD and examples from the state of
practice. The tutorial provides an introduction to KDD, defines the
basic terms and the relation between data mining and the KDD process,
presents methods for data preparation and preprocessing, describes major
data mining techniques from the fields of AI, pattern recognition,
databases, and visualization, discusses major KDD systems from academia
and industry, and provides a guide for developing a KDD system. In the
process, the tutorial addresses such issues as role played by the
various steps in the KDD process, e.g., sampling, data selection,
projection and dimensionality reduction, extraction of patterns and
models, and the use of extracted knowledge in decision- making.
There are no pre-requisites for this tutorial other than familiarity
with basic concepts in AI.
About the Lecturers
is a Senior Researcher at Microsoft Research, a Distinguished Visiting
Scientist at the Jet Propulsion Laboratory, Caltech, and an adjunct
professor of computer science at University of Southern
California. Prior to joining Microsoft he headed the Machine Learning
Systems Group at JPL. He received his Ph.D. in Computer Science (1991)
from the University of Michigan, Ann Arbor. He was program cochairman of
KDD-94 and KDD-95, general chair of KDD-96, and Editor-in- Chief of the
Journal of Knowledge Discovery and Data Mining.
Evangelos Simoudis is Vice President of Decision Support Solutions at IBM and an adjunct professor of computer engineering at the Santa Clara University. Prior to joining IBM, Dr. Simoudis led the development and market introduction of the Recon data mining system, and led research on knowledge discovery in databases, and machine learning. Dr. Simoudis received his Ph.D. in Computer Science from Brandeis University. He is Editor-in-Chief of the Artificial Intelligence Review, and has served as Program cochairman of KDD-96.