Editors:
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2168)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
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Table of contents (45 papers)
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Front Matter
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Regular Papers
About this book
The 40 revised full papers presented together with four invited contributions were carefully reviewed and selected from close to 100 submissions. Among the topics addressed are hidden Markov models, text summarization, supervised learning, unsupervised learning, demographic data analysis, phenotype data mining, spatio-temporal clustering, Web-usage analysis, association rules, clustering algorithms, time series analysis, rule discovery, text categorization, self-organizing maps, filtering, reinforcemant learning, support vector machines, visual data mining, and machine learning.
Editors and Affiliations
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Department of Computer Science, Albert-Ludwigs University Freiburg, Freiburg, Germany
Luc Raedt
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Inst.of Information and Computing Sciences Dept. of Mathematics and Computer Science, University of Utrecht, TB Utrecht, The Netherlands
Arno Siebes
Bibliographic Information
Book Title: Principles of Data Mining and Knowledge Discovery
Book Subtitle: 5th European Conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001 Proceedings
Editors: Luc Raedt, Arno Siebes
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/3-540-44794-6
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 2001
Softcover ISBN: 978-3-540-42534-2Published: 23 August 2001
eBook ISBN: 978-3-540-44794-8Published: 30 June 2003
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: DXXXII, 514
Topics: Artificial Intelligence, Data Structures and Information Theory, Database Management, IT in Business, Information Storage and Retrieval, Natural Language Processing (NLP)