Relational Data Mining

By: Saso Dzeroski (Editor), Nada LavraÄ� (Editor)

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Hardcover | 1 August 2001

At a Glance

Hardcover
420 Pages

Dimensions(cm)
24.13 x 16.51 x 1.91

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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.

Industry Reviews

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)

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Computer Programming & Software Development Business & Management Business Mathematics & Systems Computer Science Artificial Intelligence Computer Vision

Introduction
Data Mining in a Nutshell	p. 3
Knowledge Discovery in Databases: An Overview	p. 28
An Introduction to Inductive Logic Programming	p. 48
Inductive Logic Programming for Knowledge Discovery in Databases	p. 74
Techniques
Three Companions for Data Mining in First Order Logic	p. 105
Inducing Classification and Regression Trees in First Order Logic	p. 140
Relational Rule Induction with CProgol4.4: A Tutorial Introduction	p. 160
Discovery of Relational Association Rules	p. 189
Distance Based Approaches to Relational Learning and Clustering	p. 213
From Propositional to Relational Data Mining
How to Upgrade Propositional Learners to First Order Logic: A Case Study	p. 235
Propositionalization Approaches to Relational Data Mining	p. 262
Relational Learning and Boosting	p. 292
Learning Probabilistic Relational Models	p. 307
Applications and Web Resources
Relational Data Mining Applications: An Overview	p. 339
Four Suggestions and a Rule Concerning the Application of ILP	p. 365
Internet Resources on ILP for KDD	p. 375
Author Index	p. 389
Subject Index	p. 391
Table of Contents provided by Blackwell. All Rights Reserved.

Relational Data Mining

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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