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Quality Measures in Data Mining

Overview of attention for book
Cover of 'Quality Measures in Data Mining'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Choosing the Right Lens: Finding What is Interesting in Data Mining
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    Chapter 2 A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study
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    Chapter 3 Association Rule Interestingness Measures: Experimental and Theoretical Studies
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    Chapter 4 On the Discovery of Exception Rules: A Survey
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    Chapter 5 Measuring and Modelling Data Quality for Quality-Awareness in Data Mining
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    Chapter 6 Quality and Complexity Measures for Data Linkage and Deduplication
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    Chapter 7 Statistical Methodologies for Mining Potentially Interesting Contrast Sets
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    Chapter 8 Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality
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    Chapter 9 A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link
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    Chapter 10 Towards a Unifying Probabilistic Implicative Normalized Quality Measure for Association Rules
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    Chapter 11 Association Rule Interestingness: Measure and Statistical Validation
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    Chapter 12 Comparing Classification Results between N-ary and Binary Problems
Attention for Chapter 9: A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link
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Citations

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Chapter title
A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link
Chapter number 9
Book title
Quality Measures in Data Mining
Published by
Springer, Berlin, Heidelberg, January 2007
DOI 10.1007/978-3-540-44918-8_9
Book ISBNs
978-3-54-044911-9, 978-3-54-044918-8
Authors

Israel-César Lerman, Jérôme Azé

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 67%
Student > Doctoral Student 1 33%
Readers by discipline Count As %
Business, Management and Accounting 1 33%
Computer Science 1 33%
Engineering 1 33%

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