Relational Data Mining

Başlık:

ISBN:

9783662045992

Edition:

1st ed. 2001.

Yayın Bilgileri:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2001.

Fiziksel Tanımlama:

XIX, 398 p. online resource.

Contents:

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.

Abstract:

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.

Subject Term:

Data mining.

Artificial intelligence.

Data structures (Computer science).

Information theory.

Database management.

Pattern recognition systems.

Information storage and retrieval systems.

Data Mining and Knowledge Discovery.

Data Structures and Information Theory.

Automated Pattern Recognition.

Information Storage and Retrieval.