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Principles of Data Mining

Overview of attention for book
Cover of 'Principles of Data Mining'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction to Data Mining
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    Chapter 2 Data for Data Mining
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    Chapter 3 Introduction to Classification: Naïve Bayes and Nearest Neighbour
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    Chapter 4 Using Decision Trees for Classification
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    Chapter 5 Decision Tree Induction: Using Entropy for Attribute Selection
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    Chapter 6 Decision Tree Induction: Using Frequency Tables for Attribute Selection
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    Chapter 7 Estimating the Predictive Accuracy of a Classifier
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    Chapter 8 Continuous Attributes
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    Chapter 9 Avoiding Overfitting of Decision Trees
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    Chapter 10 More About Entropy
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    Chapter 11 Inducing Modular Rules for Classification
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    Chapter 12 Measuring the Performance of a Classifier
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    Chapter 13 Dealing with Large Volumes of Data
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    Chapter 14 Ensemble Classification
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    Chapter 15 Comparing Classifiers
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    Chapter 16 Association Rule Mining I
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    Chapter 17 Association Rule Mining II
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    Chapter 18 Association Rule Mining III: Frequent Pattern Trees
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    Chapter 19 Clustering
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    Chapter 20 Text Mining
Attention for Chapter 17: Association Rule Mining II
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Citations

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Chapter title
Association Rule Mining II
Chapter number 17
Book title
Principles of Data Mining
Published by
Springer, London, January 2013
DOI 10.1007/978-1-4471-4884-5_17
Book ISBNs
978-1-4471-4883-8, 978-1-4471-4884-5
Authors

Max Bramer

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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 %
Researcher 1 20%
Unknown 4 80%
Readers by discipline Count As %
Earth and Planetary Sciences 1 20%
Unknown 4 80%

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