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Inductive Databases and Constraint-Based Data Mining

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Cover of 'Inductive Databases and Constraint-Based Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Inductive Databases and Constraint-based Data Mining: Introduction and Overview
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    Chapter 2 Representing Entities in the OntoDM Data Mining Ontology
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    Chapter 3 A Practical Comparative Study Of Data Mining Query Languages
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    Chapter 4 A Theory of Inductive Query Answering
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    Chapter 5 Generalizing Itemset Mining in a Constraint Programming Setting
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    Chapter 6 From Local Patterns to Classification Models
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    Chapter 7 Constrained Predictive Clustering
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    Chapter 8 Finding Segmentations of Sequences
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    Chapter 9 Mining Constrained Cross-Graph Cliques in Dynamic Networks
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    Chapter 10 Probabilistic Inductive Querying Using ProbLog
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    Chapter 11 Inductive Querying with Virtual Mining Views
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    Chapter 12 SINDBAD and SiQL: Overview, Applications and Future Developments
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    Chapter 13 Patterns on Queries
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    Chapter 14 Experiment Databases
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    Chapter 15 Predicting Gene Function using Predictive Clustering Trees
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    Chapter 16 Analyzing Gene Expression Data with Predictive Clustering Trees
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    Chapter 17 Using a Solver Over the String Pattern Domain to Analyze Gene Promoter Sequences
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    Chapter 18 Inductive Queries for a Drug Designing Robot Scientist
Attention for Chapter 10: Probabilistic Inductive Querying Using ProbLog
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Citations

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Summary Dimensions citations
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Chapter title
Probabilistic Inductive Querying Using ProbLog
Chapter number 10
Book title
Inductive Databases and Constraint-Based Data Mining
Published in
ADS, January 2010
DOI 10.1007/978-1-4419-7738-0_10
Book ISBNs
978-1-4419-7737-3, 978-1-4419-7738-0
Authors

Luc De Raedt, Angelika Kimmig, Bernd Gutmann, Kristian Kersting, Vítor Santos Costa, Hannu Toivonen

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 25%
Student > Doctoral Student 2 17%
Researcher 2 17%
Student > Master 2 17%
Professor 1 8%
Other 0 0%
Unknown 2 17%
Readers by discipline Count As %
Computer Science 7 58%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Engineering 1 8%
Unknown 2 17%

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