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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 9: Mining Constrained Cross-Graph Cliques in Dynamic Networks
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Citations

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Chapter title
Mining Constrained Cross-Graph Cliques in Dynamic Networks
Chapter number 9
Book title
Inductive Databases and Constraint-Based Data Mining
Published in
ADS, January 2010
DOI 10.1007/978-1-4419-7738-0_9
Book ISBNs
978-1-4419-7737-3, 978-1-4419-7738-0
Authors

Loïc Cerf, Bao Tran Nhan Nguyen, Jean-François Boulicaut

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 83%
Professor > Associate Professor 1 17%
Readers by discipline Count As %
Computer Science 4 67%
Unknown 2 33%

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