
Résumé
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
Introduction
- Data mining in a nutshell
- Knowledge discoverfy indatabase : an overview
- An introduction to inductive logic programming
- Inductive logic programming for knowledge discovery in databases
- Three companions for data mining in first order logic
- Inducing classification and regression trees in first order logic
- Relational rule induction with CProgol4.4 : a tutorial induction
- Discovery of relational associations rules
- Distance based approaches to relational learnijng and clustering
- How to upgrade propositional learners to first order logic : a case study
- Propositionalization approaches to relational data mining
- Relatioanl leraning and boosting
- Learning probalistic relational models
- Relational data minig applications : an overview
- Four suggestions and a rule concerning the application of ILP
- Internet resources on ILP for KDD
- Index
Caractéristiques techniques
PAPIER | |
Éditeur(s) | Springer |
Auteur(s) | Saso Dzeroski, Nada Lavrac |
Parution | 19/10/2001 |
Nb. de pages | 398 |
Format | 16 x 24 |
Couverture | Relié |
Poids | 710g |
Intérieur | Noir et Blanc |
EAN13 | 9783540422891 |
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