R and Data Mining: Examples and Case Studies

R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.

Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.

With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis.

Presents an introduction into using R for data mining applications, covering most popular data mining techniquesProvides code examples and data so that readers can easily learn the techniquesFeatures case studies in real-world applications to help readers apply the techniques in their work

1. Introduction

1. Introduction, Data mining

1. R
2. Datasets used in this book
2. Data Loading and Exploration

1. Data Import/Export

1. Save/Load R Data
2. Import from and Export to .CSV Files
3. Import Data from SAS
4. Import/Export via ODBC
2. Data Exploration

1. Have a Look at Data
2. Explore Individual Variables
3. Explore Multiple Variables
4. More Exploration
5. Save Charts as Files
3. Data Mining Examples

1. Decision Trees

1. Building Decision Trees with Package party
2. Building Decision Trees with Package rpart
3. Random Forest
2. Regression

1. Linear Regression
2. Logistic Regression
3. Generalized Linear Regression
4. Non-linear Regression
3. Clustering

1. K-means Clustering
2. Hierarchical Clustering
3. Density-based Clustering
4. Outlier Detection
5. Time Series Analysis

1. Time Series Decomposition
2. Time Series Forecast
6. Association Rules
7. Sequential Patterns
8. Text Mining
9. Social Network Analysis
4. Case Studies

1. Case Study I: Analysis and Forecasting of House Price Indices

1. Reading Data from a CSV File
2. Data Exploration
3. Time Series Decomposition
4. Time Series Forecasting
5. Discussion
2. Case Study II: Customer Response Prediction
3. Case Study III: Risk Rating using Decision Tree with Limited Resources
4. Customer Behaviour Prediction and Intervention
5. Appendix

1. Online Resources
2. R Reference Card for Data Mining

Bibliography