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Relational Data Mining
 
 
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Relational Data Mining [Hardcover]

Saso Dzeroski (Editor), Nada Lavra (Editor)

Price: $119.00 & this item ships for FREE with Super Saver Shipping. Details
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Book Description

October 2, 2001
As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. 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.

Editorial Reviews

Review

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)

From the Back Cover

As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. 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.

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Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
prune statements, signal structure classes, relational background knowledge, dlab variable, relational data mining tasks, relational association rules, relational rule induction, class dependency graph, most specific clause, protein functional class, genome scale prediction, first order logical decision trees, relational learning problem, guaranteed acyclic, diterpene structure elucidation, inverse resolvent, predicting biodegradability, propositional learner, clause daughter, involve multiple relations, subgroup discovery, bottom clause, utility predicates, inductive logic programming systems, probabilistic relational models
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, International Conference, International Workshop, San Mateo, San Francisco, Van Laer, Menlo Park, European Conference, New Generation Computing, New York, Department of Computer Science, Nada Lavrac, Katholieke Universiteit Leuven, Academic Press, Intelligent Systems, Stefan Kramer, Algorithmic Learning Theory, Paymt Mode, Social Status, Ellis Horwood, Journal of Logic Programming, Jozef Stefan Institute, Protein Engineering, Zip Sex, Computer Society Press
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