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Relational Data Mining
Editor(s) :Saso dzeroski, Nada Lavrac

ISBN : 9788132202271
Name : Relational Data Mining
Price : 895.00
Editor/s : Saso dzeroski, Nada Lavrac
Type : Text Book
Pages : 398
Year of Publication : Rpt.2011
Publisher : Springer/BSP Books
Binding : Paperback
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About the Book

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.


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

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