From Data Mining to Knowledge Discovery in Databases

addison wesley weiss s i and kulikowski c 1991

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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.: Morgan Kaufmann. 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...
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