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Local Pattern Detection

Overview of attention for book
Cover of 'Local Pattern Detection'

Table of Contents

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    Book Overview
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    Chapter 1 Pushing Constraints to Detect Local Patterns
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    Chapter 2 From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms
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    Chapter 3 Pattern Discovery Tools for Detecting Cheating in Student Coursework
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    Chapter 4 Local Pattern Detection and Clustering
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    Chapter 5 Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery
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    Chapter 6 Visualizing Very Large Graphs Using Clustering Neighborhoods
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    Chapter 7 Features for Learning Local Patterns in Time-Stamped Data
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    Chapter 8 Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis
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    Chapter 9 Local Pattern Discovery in Array-CGH Data
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    Chapter 10 Learning with Local Models
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    Chapter 11 Knowledge-Based Sampling for Subgroup Discovery
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    Chapter 12 Temporal Evolution and Local Patterns
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    Chapter 13 Undirected Exception Rule Discovery as Local Pattern Detection
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    Chapter 14 From Local to Global Analysis of Music Time Series
Attention for Chapter 8: Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis
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Citations

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Chapter title
Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis
Chapter number 8
Book title
Local Pattern Detection
Published by
Springer, Berlin, Heidelberg, January 2005
DOI 10.1007/11504245_8
Book ISBNs
978-3-54-026543-6, 978-3-54-031894-1
Authors

Ruggero G. Pensa, Jean-François Boulicaut, Pensa, Ruggero G., Boulicaut, Jean-François

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 20%
Student > Bachelor 1 20%
Student > Ph. D. Student 1 20%
Student > Master 1 20%
Professor > Associate Professor 1 20%
Other 0 0%
Readers by discipline Count As %
Computer Science 4 80%
Physics and Astronomy 1 20%

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