Saso Dzeroski / Nada Lavrac (eds.)

Relational Data Mining

Herausgegeben:Dzeroski, Saso; Lavrac, Nada

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-540-42289-1
• 2001
• Seitenzahl: 420
• Erscheinungstermin: 1. August 2001
• Englisch
• Abmessung: 241mm x 160mm x 28mm
• Gewicht: 784g
• ISBN-13: 9783540422891
• ISBN-10: 3540422897
• Artikelnr.: 10117618

Inhaltsangabe

I. Introduction.- 1. Data Mining in a Nutshell.- 2. Knowledge Discovery in Databases: An Overview.- 3. An Introduction to Inductive Logic Programming.- 4. Inductive Logic Programming for Knowledge Discovery in Databases.- II. Techniques.- 5. Three Companions for Data Mining in First Order Logic.- 6. Inducing Classification and Regression Trees in First Order Logic.- 7. Relational Rule Induction with CProgol4.4: A Tutorial Introduction.- 8. Discovery of Relational Association Rules.- 9. Distance Based Approaches to Relational Learning and Clustering.- III. From Propositional to Relational Data Mining.- 10. How to Upgrade Propositional Learners to First Order Logic: A Case Study.- 11. Propositionalization Approaches to Relational Data Mining.- 12. Relational Learning and Boosting.- 13. Learning Probabilistic Relational Models.- IV. Applications and Web Resources.- 14. Relational Data Mining Applications: An Overview.- 15. Four Suggestions and a Rule Concerning the Application of ILP.- 16. Internet Resources on ILP for KDD.- Author Index.

Rezensionen

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)