This preview shows page 1. Sign up to view the full content.
Unformatted text preview: Temporal Data Mining of Large Geophysical Datasets. In
Proceedings of KDD-95: First International Conference on Knowledge Discovery and Data Mining,
300–305. Menlo Park, Calif.: American Association
for Artiﬁcial Intelligence.
Titterington, D. M.; Smith, A. F. M.; and Makov, U.
E. 1985. Statistical Analysis of Finite-Mixture Distributions. Chichester, U.K.: Wiley.
U.S. News. 1995. Basketball’s New High-Tech Guru:
IBM Software Is Changing Coaches’ Game Plans.
U.S. News and World Report, 11 December.
Weigend, A., and Gershenfeld, N., eds. 1993. Predicting the Future and Understanding the Past. Redwood City, Calif.: Addison-Wesley.
Weiss, S. I., and Kulikowski, C. 1991. Computer Systems That Learn: Classiﬁcation and Prediction Methods from Statistics, Neural Networks, Machine Learning, and Expert Systems. S an Francisco, Calif.:
Whittaker, J. 1990. Graphical Models in Applied Multivariate Statistics. New York: Wiley.
Zembowicz, R., and Zytkow, J. 1996. From Contingency Tables to Various Forms of Knowledge in
Databases. In Advances in Knowledge Discovery and
Data Mining, eds. U. Fayyad, G. Piatetsky-Shapiro, P.
Smyth, and R. Uthurusamy, 329–351. Menlo Park,
Calif.: AAAI Press. Usama Fayyad i s a senior researcher at Microsoft Research.
He received his Ph.D. in 1991
from the University of Michigan
at Ann Arbor. Prior to joining Microsoft in 1996, he headed the
Machine Learning Systems Group
at the Jet Propulsion Laboratory
(JPL), California Institute of Technology, where he developed data-mining systems
for automated science data analysis. He remains
afﬁliated with JPL as a distinguished visiting scientist. Fayyad received the JPL 1993 Lew Allen Award
for Excellence in Research and the 1994 National
Aeronautics and Space Administration Exceptional
Achievement Medal. His research interests include
knowledge discovery in large databases, data mining, machine-learning theory and applications, statistical pattern recognition, and clustering. He was
program cochair of KDD-94 and KDD-95 (the First
International Conference on Knowledge Discovery
and Data Mining). He is general chair of KDD-96,
an editor in chief of the journal Data Mining and
Knowledge Discovery, and coeditor of the 1996 AAAI
Press book Advances in Knowledge Discovery and Data Mining. FALL 1996 53 Articles
Gregory Piatetsky-Shapiro i s a
principal member of the technical
staff at GTE Laboratories and the
principal investigator of the
Knowledge Discovery in Databases (KDD) Project, which focuses
on developing and deploying advanced KDD systems for business
applications. Previously, he
worked on applying intelligent front ends to heterogeneous databases. Piatetsky-Shapiro received
several GTE awards, including GTE’s highest technical achievement award for the KEﬁR system for
health-care data analysis. His research interests include intelligent database systems, dependency
networks, and Internet resource discovery. Prior to
GTE, he worked at Strategic Information developing ﬁnancial database systems. Piatetsky-Shapiro received his M.S. in 1979 and his Ph.D. in 1984, both
from New York University (NYU). His Ph.D. dissertation on self-organizing da...
View Full Document
- Spring '14