Data Mining for Business Analytics: Concepts, Techniques and Applications in R

Part I: Preliminaries
1. Introduction
2. Overview of the data mining process

Part II: Data Exploration and Dimension Reduction
3. Data visualization
4. Dimension reduction

Part III: Performance Evaluation
5. Evaluating predictive performance

Part IV: Prediction and Classification Methods
6. Multiple linear regression
7. k-Nearest Neighbors (kNN)
8. The Naive Bayes classifier
9. Classification and regression trees
10. Logistic regression
11. Neural nets
12. Discriminant analysis
13. Combining methods : ensembles and uplift modeling

Part V: Mining Relationships Among Records
14. Association rules and collaborative filtering
15. Cluster analysis

Part VI: Forecasting Time Series
16. Handling time series
17. Regression-based forecasting
18. Smoothing methods

Part VII: Data Analytics
19. Social network analytics
20. Text mining

Part VIII: Cases
21. Cases