Relational Data Mining

1. Front Matter

   Pages I-XIX

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2. Introduction

   1. Front Matter

      Pages 1-1

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   2. Data Mining in a Nutshell

      • Sašo Džeroski

      Pages 3-27

   3. Knowledge Discovery in Databases: An Overview

      • Usama Fayyad

      Pages 28-47

   4. An Introduction to Inductive Logic Programming

      • Sašo Džeroski, Nada Lavrač

      Pages 48-73

   5. Inductive Logic Programming for Knowledge Discovery in Databases

      • Stefan Wrobel

      Pages 74-101

3. Techniques

   1. Front Matter

      Pages 103-103

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   2. Three Companions for Data Mining in First Order Logic

      • Luc De Raedt, Hendrik Blockeel, Luc Dehaspe, Wim Van Laer

      Pages 105-139

   3. Inducing Classification and Regression Trees in First Order Logic

      • Stefan Kramer, Gerhard Widmer

      Pages 140-159

   4. Relational Rule Induction with CProgol4.4: A Tutorial Introduction

      • Stephen Muggleton, John Firth

      Pages 160-188

   5. Discovery of Relational Association Rules

      • Luc Dehaspe, Hannu Toivonen

      Pages 189-212

   6. Distance Based Approaches to Relational Learning and Clustering

      • Mathias Kirsten, Stefan Wrobel, Tamás Horváth

      Pages 213-232

4. From Propositional to Relational Data Mining

   1. Front Matter

      Pages 233-233

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   2. How to Upgrade Propositional Learners to First Order Logic: A Case Study

      • Wim Van Laer, Luc De Raedt

      Pages 235-261

   3. Propositionalization Approaches to Relational Data Mining

      • Stefan Kramer, Nada Lavrač, Peter Flach

      Pages 262-291

   4. Relational Learning and Boosting

      • Ross Quinlan

      Pages 292-306

   5. Learning Probabilistic Relational Models

      • Lise Getoor, Nir Friedman, Daphne Koller, Avi Pfeffer

      Pages 307-335

5. Applications and Web Resources

   1. Front Matter

      Pages 337-337

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   2. Relational Data Mining Applications: An Overview

      • Sašo Džeroski

      Pages 339-364

   3. Four Suggestions and a Rule Concerning the Application of ILP

      • Ashwin Srinivasan

      Pages 365-374

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.

• Algorithmic Learning
• Data Analysis
• Data Mining
• Inductive Logic Programming
• Knowledge Discovery
• Knowledge Processing
• Machine Learning
• Multi-Relational Data
• classification
• database
• knowledge
• learning
• logic
• logic programming
• programming

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)

• Jožef Stefan Institute, Ljubljana, Slovenia

  Sašo Džeroski, Nada Lavrač

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