Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.

Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:

• A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools
• Illustrations of how to use the outlined concepts in real-world situations
• Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials
• Numerous exercises to help readers with computing skills and deepen their understanding of the material

Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

JOHANNES LEDOLTER, PhD, is Professor in both the Department of Management Sciences and the Department of Statistics and Actuarial Science at the University of Iowa. He is a Fellow of the American Statistical Association and the American Society for Quality, and an Elected Member of the International Statistical Institute. Dr. Ledolter is the coauthor of Statistical Methods for Forecasting, Achieving Quality Through Continual Improvement, and Statistical Quality Control: Strategies and Tools for Continual Improvement, all published by Wiley.

Preface

Acknowledgements

1. Introduction

Reference

2. Processing the Information and Getting to Know Your Data

2.1 Example 1: 2006 Birth Data

2.2 Example 2: Alumni Donations

2.3 Example 3: Orange Juice

References

3. Standard Linear Regression

3.1 Example 1: Fuel Efficiency of Automobiles

3.2 Example 2: Toyota Used Car Prices

Appendix: The Effects of Model Over fitting on the Average Mean Square Error of the Regression Prediction

References

4. Local Polynomial Regression: A Nonparametric Regression Approach

4.1 Example 1: Old Faithful

4.2 Example 2: NOx Exhaust Emissions

References

5. Importance of Parsimony in Statistical Modeling

References

6. Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO)

6.1 Example 1: Prostate Cancer

6.2 Example 2: Orange Juice References

7. Logistic Regression

7.1 Example 1: Death Penalty Data

7.2 Example 2: Delayed Airplanes

7.3 Example 3: Loan Acceptance

7.4 Example 4: German Credit Data

References

8. Binary Classification, Probabilities and Evaluating Classification Performance

8.1 Example: German Credit Data

References

9. Classification Using a Nearest Neighbor Analysis

9.1 Example 1: Forensic Glass

9.2 Example 2: German Credit Data

10. The Naïve Bayesian Analysis: A Model for Predicting a Categorical Response from Mostly Categorical Predictor Variables

10.1 Example: Delayed Airplanes

Reference

11. Multinomial Logistic Regression

11.1 Example 1: Forensic Glass

11.2 Example 2: Forensic Glass Revisited

Appendix: Specification of a Simple Triplet Matrix

References

12. More on Classification and a Discussion of Discriminant Analysis 12.1 Example 1: German Credit Data

12.2 Example 2: Fisher Iris Data

12.3 Example 3: Forensic Glass Data

12.4 Example 4: MBA Admission Data

Reference

13. Decision Trees

13.1Example 1: Prostate Cancer

13.2 Example 2: Motorcycle Acceleration

13.3 Example 3: Fisher Iris Data Revisited

14. Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods

14.1 R Packages for Tree Construction

14.2 CHAID

14.3Ensemble Methods: Bagging, Boosting, and Random Forests

14.4Support Vector Machines (SVM)

14.5Neural Networks

14.6The R Package rattle: A Useful Graphical User Interface for Data Mining

References

15. Clustering

15.1 k-means Clustering

15.2Another Way to Look at Clustering: Applying the Expectation Maximization (EM) Algorithm to Mixtures of Normal Distributions

15.3 Hierarchical Clustering Procedures

References

16. Market Basket Analysis: Association Rules and Lift

16.1 Example 1: Online Radio 16.2 Example

2: Predicting Income

References

17. Dimension Reduction: Factor Models and Principal Components 17.1 Example 1: European Protein Consumption

17.2 Example 2: Monthly US Unemployment Rates

18. Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares

18.1 Example 1: Generated Data

18.2 Example 2: Predicting Next Month’s Unemployment Rate of a Certain State from Past Unemployment Rates of All 50 States

18.3 Example 3: Predicting Next Month’s Unemployment Rate. Comparing Several Methods in Terms of their Out-Of-Sample Prediction Performance

References

19. Text as Data: Text Mining and Sentiment Analysis

19.1 Example 1: Restaurant Reviews

19.2 Example 2: Political Sentiment

Appendix: Relationship between the Gentzkow/Shapiro Estimate of “Slant” and Partial Least Squares

References

20. Analysis of Network Data

20.1 Example 1: Marriage and Power in 15^th Century Florence

20.2 Example 2: Connections in a Friendship Network

References

Appendix: Exercises

Exercises 1

Exercises 2

Exercises 3

Exercises 4

Exercise 5

Exercise 6

Exercises 7

Appendix: References

Index