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Relational Data Mining

Overview of attention for book
Cover of 'Relational Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Data Mining in a Nutshell
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    Chapter 2 Knowledge Discovery in Databases: An Overview
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    Chapter 3 An Introduction to Inductive Logic Programming
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    Chapter 4 Inductive Logic Programming for Knowledge Discovery in Databases
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    Chapter 5 Three Companions for Data Mining in First Order Logic
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    Chapter 6 Inducing Classification and Regression Trees in First Order Logic
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    Chapter 7 Relational Rule Induction with CP rogol 4.4: A Tutorial Introduction
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    Chapter 8 Discovery of Relational Association Rules
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    Chapter 9 Distance Based Approaches to Relational Learning and Clustering
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    Chapter 10 How to Upgrade Propositional Learners to First Order Logic: A Case Study
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    Chapter 11 Propositionalization Approaches to Relational Data Mining
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    Chapter 12 Relational Learning and Boosting
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    Chapter 13 Learning Probabilistic Relational Models
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    Chapter 14 Relational Data Mining Applications: An Overview
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    Chapter 15 Four Suggestions and a Rule Concerning the Application of ILP
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    Chapter 16 Internet Resources on ILP for KDD
Attention for Chapter 7: Relational Rule Induction with CP rogol 4.4: A Tutorial Introduction
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Citations

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Chapter title
Relational Rule Induction with CP rogol 4.4: A Tutorial Introduction
Chapter number 7
Book title
Relational Data Mining
Published by
Springer, Berlin, Heidelberg, January 2001
DOI 10.1007/978-3-662-04599-2_7
Book ISBNs
978-3-64-207604-6, 978-3-66-204599-2
Authors

Stephen Muggleton, John Firth, Muggleton, Stephen, Firth, John

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 57%
Student > Ph. D. Student 2 29%
Professor > Associate Professor 1 14%
Readers by discipline Count As %
Computer Science 6 86%
Engineering 1 14%

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