Features
- Cover Type: Paperback with 508 pages
- Published by: Springer
- Edition: 1st Edition November 5, 2003
- Written in: English
- ISBN 10 Number: 3540200851
- ISBN 13 Number: 978-3540200857
-
Book Dimensions:
9.2 x 6.1 x 1.1 inches
- Weighs: 1.6 pounds
Product Description
This book constitutes the refereed proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with ECML 2003.
The forty revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another forty ones for ECML 2003, selected from a total of 332 submissions. The papers address all current issues in data mining and knowledge discovery in databases including data mining tools, association rule mining, classification, clustering, pattern mining, multi-relational classifiers, boosting, kernel methods, learning Bayesian networks, inductive logic programming, user preferences mining, time series analysis, multi-view learning, support vector machine, pattern mining, relational learning, categorization, information extraction, decision making, prediction, and decision trees.
About The Author
This book constitutes the refereed proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with ECML 2003.
The forty revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another forty ones for ECML 2003, selected from a total of 332 submissions. The papers address all current issues in data mining and knowledge discovery in databases including data mining tools, association rule mining, classification, clustering, pattern mining, multi-relational classifiers, boosting, kernel methods, learning Bayesian networks, inductive logic programming, user preferences mining, time series analysis, multi-view learning, support vector machine, pattern mining, relational learning, categorization, information extraction, decision making, prediction, and decision trees.
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