Editors:
- Provides a broad and unifying perspective on the field of data mining in general and inductive databases in particular
- Includes constraint-based mining of predictive models for structured data/outputs, integration/unification of pattern and model mining at the conceptual level
- Discusses applications to practically relevant problems in bioinformatics
- Includes supplementary material: sn.pub/extras
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Table of contents (18 chapters)
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Front Matter
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Constraint-based Mining: Selected Techniques
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Front Matter
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Inductive Databases: Integration Approaches
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Front Matter
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Applications
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Front Matter
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About this book
Editors and Affiliations
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, Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia
Sašo Džeroski
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, Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium
Bart Goethals
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, Dept. of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia
Panče Panov
Bibliographic Information
Book Title: Inductive Databases and Constraint-Based Data Mining
Editors: Sašo Džeroski, Bart Goethals, Panče Panov
DOI: https://doi.org/10.1007/978-1-4419-7738-0
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Science+Business Media, LLC 2010
Hardcover ISBN: 978-1-4419-7737-3
Softcover ISBN: 978-1-4899-8217-9
eBook ISBN: 978-1-4419-7738-0
Edition Number: 1
Number of Pages: XVII, 456
Topics: Database Management, Data Mining and Knowledge Discovery, Artificial Intelligence, Computational Biology/Bioinformatics