Data Mining for Business Analytics_ Conc.epub - (EPUB全文下载)

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CONTENTS
Cover
Title Page
Copyright
Dedication
Foreword
Preface to the Third Edition
Preface to the First Edition
Acknowledgments
Part I: Preliminaries
Chapter 1: Introduction
1.1 What is Business Analytics?
1.2 What is Data Mining?
1.3 Data Mining and Related Terms
1.4 Big Data
1.5 Data Science
1.6 Why are There so Many Different Methods?
1.7 Terminology and Notation
1.8 Road Maps to This Book
Chapter 2: Overview of the Data Mining Process
2.1 Introduction
2.2 Core Ideas in Data Mining
2.3 The Steps in Data Mining
2.4 Preliminary Steps
2.5 Predictive Power and Overfitting
2.6 Building a Predictive Model with XLMiner
2.7 Using Excel for Data Mining
2.8 Automating Data Mining Solutions
Problems
Part II: Data Exploration and Dimension Reduction
Chapter 3: Data Visualization
3.1 Uses of Data Visualization
3.2 Data Examples
3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots
3.4 Multidimensional Visualization
3.5 Specialized Visualizations
3.6 Summary: Major Visualizations and Operations, by Data Mining Goal
Problems
Chapter 4: Dimension Reduction
4.1 Introduction
4.2 Curse of Dimensionality
4.3 Practical Considerations
4.4 Data Summaries
4.5 Correlation Analysis
4.6 Reducing the Number of Categories in Categorical Variables
4.7 Converting a Categorical Variable to a Numerical Variable
4.8 Principal Components Analysis
4.9 Dimension Reduction Using Regression Models
4.10 Dimension Reduction Using Classification and Regression Trees
Problems
Part III: Performance Evaluation
Chapter 5: Evaluating Predictive Performance
5.1 Introduction
5.2 Evaluating Predictive Performance
5.3 Judging Classifier Performance
5.4 Judging Ranking Performance
5.5 Oversampling
Problems
Part IV: Prediction and Classification Methods
Chapter 6: Multiple Linear Regression
6.1 Introduction
6.2 Explanatory vs. Predictive Modeling
6.3 Estimating the Regression Equation and Prediction
6.4 Variable Selection in Linear Regression
Problems
Chapter 7: k-Nearest-Neighbors (k-NN)
7.1 The k-NN Classifier (Categorical Outcome)
7.2 k-NN for a Numerical Response
7.3 Advantages and Shortcomings of k-NN Algorithms
Problems
Chapter 8: The Naive Bayes Classifier
8.1 Introduction
8.2 Applying the Full (Exact) Bayesian Classifier
8.3 Advantages and Shortcomings of the Naive Bayes Classifier
Problems
Chapter 9: Classification and Regression Trees
9.1 Introduction
9.2 Classification Trees
9.3 Evaluating the Performance of a Classification Tree
9.4 Avoiding Overfitting
9.5 Classification Rules from T ............

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