Relational Data Mining

1. Auflage 2001
ISBN: 978-3-540-42289-1
Verlag: Springer

Buch, Englisch, 398 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1680 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Dzeroski, Saso

Lavrac, Nada

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
• Wirtschaftswissenschaften Wirtschaftswissenschaften Unternehmensgeschichte, Einzelne Branchen und Unternehmer
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Zeichen- und Zahlendarstellungen
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmierung: Methoden und Allgemeines

Inhaltsverzeichnis

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.