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.

t. I. Introduction. 1. Data Mining in a Nutshell / Saso Dzeroski. 2. Knowledge Discovery in Databases: An Overview / Usama Fayyad. 3. An Introduction to Inductive Logic Programming / Saso Dzeroski and Nada Lavrac. 4. Inductive Logic Programming for Knowledge Discovery in Databases / Stefan Wrobel -- Pt. II. Techniques. 5. Three Companions for Data Mining in First Order Logic / Luc De Raedt, Hendrik Blockeel and Luc Dehaspe / [et al.]. 6. Inducing Classification and Regression Trees in First Order Logic / Stefan Kramer and Gerhard Widmer. 7. Relational Rule Induction with CProgol4.4: A Tutorial Introduction / Stephen Muggleton and John Firth. 8. Discovery of Relational Association Rules / Luc Dehaspe and Hannu Toivonen.

Devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The book covers the basics of classical knowledge discovery; the techniques in relational data mining; and advanced applications in various fields.