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Principles of data mining

Overview of attention for book
Principles of data mining
Springer

Table of Contents

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    Book Overview
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    Chapter 1 Data for Data Mining
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    Chapter 2 Introduction to Classification: Na¨ive Bayes and Nearest Neighbour
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    Chapter 3 Using Decision Trees for Classification
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    Chapter 4 Decision Tree Induction: Using Entropy for Attribute Selection
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    Chapter 5 Decision Tree Induction: Using Frequency Tables for Attribute Selection
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    Chapter 6 Estimating the Predictive Accuracy of a Classifier
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    Chapter 7 Continuous Attributes
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    Chapter 8 Avoiding Overfitting of Decision Trees
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    Chapter 9 More About Entropy
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    Chapter 10 Inducing Modular Rules for Classification
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    Chapter 11 Measuring the Performance of a Classifier
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    Chapter 12 Association Rule Mining I
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    Chapter 13 Association Rule Mining II
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    Chapter 14 Clustering
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    Chapter 15 Text Mining
Attention for Chapter 15: Text Mining
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Chapter title
Text Mining
Chapter number 15
Book title
Principles of Data Mining
Published by
Springer, London, January 2007
DOI 10.1007/978-1-84628-766-4_15
Book ISBNs
978-1-84628-765-7, 978-1-84628-766-4
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