Predictive Data Mining

A Practical Guide
 
 
Morgan Kaufmann Publishers In
  • erschienen am 8. Dezember 1997
 
  • Buch
  • |
  • Softcover
  • |
  • 244 Seiten
978-1-55860-403-2 (ISBN)
 
This book presents a unified view of data mining, drawing from statistics, machine learning, and databases and focuses on the preparation of data and the development of an overall problem-solving strategy. It will interest researchers, programmers, and developers in knowledge discovery and data mining in the disciplines of AI, software engineering, and databases.
  • Englisch
  • San Francisco
  • |
  • USA
Elsevier Science & Technology
  • Für höhere Schule und Studium
black & white illustrations
  • Höhe: 230 mm
  • |
  • Breite: 152 mm
  • |
  • Dicke: 13 mm
  • 364 gr
978-1-55860-403-2 (9781558604032)
1558604030 (1558604030)
Sholom M. Weiss is a professor of computer science at Rutgers University and the author of dozens of research papers on data mining and knowledge-based systems. He is a fellow of the American Association for Artificial Intelligence, serves on numerous editorial boards of scientific journals, and has consulted widely on the commercial application of advanced data mining techniques. He is the author, with Casimir Kulikowski, of Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems, which is also available from Morgan Kaufmann Publishers. Nitin Indurkhya is on the faculty at the Basser Department of Computer Science, University of Sydney, Australia. He has published extensively on Data Mining and Machine Learning and has considerable experience with industrial data-mining applications in Australia, Japan and the USA.
1 What is Data Mining? 2 Statistical Evaluation for Big Data 3 Preparing the Data 4 Data Reduction 5 Looking for Solutions 6 What's Best for Data Reduction and Mining? 7 Art or Science? Case Studies in Data Mining
"I enjoy reading PREDICTIVE DATA MINING. It presents an excellent perspective on the theory and practice of data mining. It can help educate statisticians to build alliances between statisticians and data miners." --Emanuel Parzen, Distinguished Professor of Statistics, Texas A&M University

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