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Principles of Data Mining

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Cover of 'Principles of Data Mining'

Table of Contents

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    Book Overview
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    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
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    Chapter 21 Classifying Streaming Data
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    Chapter 22 Classifying Streaming Data II: Time-Dependent Data
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    Chapter 23 An Introduction to Neural Networks
Attention for Chapter 6: Decision Tree Induction: Using Frequency Tables for Attribute Selection
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