
Data Mining for the Masses
With Implementations in RapidMiner and R
- About the Author
- Chapter 1: Introduction to Data Mining and CRISP-DM
- Chapter 2: Organizational Understanding and Data Understanding
- Chapter 3: Data Preparation
- Chapter 4: Correlational Methods
- Chapter 5: Association Rules
- Chapter 6: k-Means Clustering
- Chapter 7: Discriminant Analysis, k-Nearest Neighbors and Naïve Bayes
- Chapter 8: Linear Regression
- Chapter 9: Logistic Regression
- Chapter 10: Decision Trees
- Chapter 11: Neural Networks
- Chapter 12: Text Mining
- Chapter 13: Evaluation and Deployment
- Chapter 14: Data Mining Ethics