Relational Data Mining

Prof. Dr. Nada Lavrac heads the Department of Knowledge Technologies at the Jo ef Stefan Institute in Ljubljana. She is the author and editor of several books and proceedings in the field of data mining and machine learning, and she has chaired or served on the boards of the main related journals and conferences. Her research interests include machine learning, data mining, and inductive logic programming, and related applications in medicine, public health, bioinformatics, and the management of virtual enterprises. In 1997 she was awarded the Ambassador of Science of Slovenia prize, and in 2007 she was elected as an ECCAI Fellow.

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.

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.