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Quality Measures in Data Mining

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Cover of 'Quality Measures in Data Mining'

Table of Contents

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    Book Overview
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    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 5: Measuring and Modelling Data Quality for Quality-Awareness in Data Mining
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