Relational Data Mining

650.00

  • Author: Saso Dzeroski
  • Co-Author: Nada Lavrac
  • Binding: Paperback
  • ISBN-13: 9788132202271
  • Pages: 398
  • Publisher: Springer India
  • Year of Pub / Reprint Year: 2011

Description

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.

Contents
Part I. Introduction
1. Data Mining in a Nutshell
2. Knowledge Discovery in Databases: An Overview
3. Introduction to Inductive Logic Programming
4. Inductive Logic Programming for Knowledge Discovery in Databases
Part 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 CPR0G0L4.4: A Tutorial Introduction
8. Discovery of Relational Association Rules
9. Distance Based Approaches to Relational Learning and Clustering
Part 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
Part 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