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Table of contents (16 chapters)
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Front Matter
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Techniques
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Front Matter
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From Propositional to Relational Data Mining
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Front Matter
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Applications and Web Resources
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Front Matter
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Keywords
About this book
This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
Reviews
From the reviews:
"The book is a collection of contributions from several authors who worked in the field. It provides quite an extensive overview of different techniques and strategies used in knowledge discovery from multi-relational data, and describes several interesting applications. … the book may stimulate the interest for practical applications of relational data mining and further research in the development of relational data mining techniques." (Marco Botta, Computer Bulletin, Vol. 46 (1), 2003)
"It is very important to describe the intersection for data mining carefully. The presented book Relational Data Mining is doing this. The authors are well known researchers in the field. … The book is recommended warmly to students of computer science and mathematics and practitioners who have to deal with data mining in relational data bases." (W. Gerhardt, Zentralblatt MATH, Vol. 1003, 2003)
Editors and Affiliations
Bibliographic Information
Book Title: Relational Data Mining
Editors: Sašo Džeroski, Nada Lavrač
DOI: https://doi.org/10.1007/978-3-662-04599-2
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 2001
Hardcover ISBN: 978-3-540-42289-1Published: 01 August 2001
Softcover ISBN: 978-3-642-07604-6Published: 15 December 2010
eBook ISBN: 978-3-662-04599-2Published: 17 April 2013
Edition Number: 1
Number of Pages: XIX, 398
Topics: Data Mining and Knowledge Discovery, Artificial Intelligence, Data Structures and Information Theory, Database Management, Pattern Recognition, Information Storage and Retrieval