Data Mining and Business Analytics with R

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.

Table of Contents

1. Introduction 1
2. Processing the Information and Getting to Know Your Data 7
3. Standard Linear Regression 40
4. Local Polynomial Regression: a Nonparametric Regression Approach 55
5. Importance of Parsimony in Statistical Modeling 67
6. Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO) 71
7. Logistic Regression 83
8. Binary Classification, Probabilities, and Evaluating Classification Performance 108
9. Classification Using a Nearest Neighbor Analysis 115
10. The Na¨yve Bayesian Analysis: a Model for Predicting a Categorical Response from Mostly Categorical
11. Multinomial Logistic Regression 132
12. More on Classification and a Discussion on Discriminant Analysis 150
13. Decision Trees 161
14. Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods 185
15. Clustering 196
16. Market Basket Analysis: Association Rules and Lift 220
17. Dimension Reduction: Factor Models and Principal Components 235
18. Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares 247
19. Text as Data: Text Mining and Sentiment Analysis 258
20. Network Data 272