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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.

Feature

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

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Introduction
 
 
Introduction, Data mining
 
 
R
 
Datasets used in this book
 
 
Data Loading and Exploration
 
 
Data Import/Export
 
 
Save/Load R Data
 
Import from and Export to .CSV Files
 
Import Data from SAS
 
Import/Export via ODBC
 
 
Data Exploration 
 
 
Have a Look at Data
 
Explore Individual Variables
 
Explore Multiple Variables
 
More Exploration
 
Save Charts as Files
 
 
Data Mining Examples
 
 
Decision Trees
 
 
Building Decision Trees with Package party
 
Building Decision Trees with Package rpart
 
Random Forest
 
 
Regression 
 
 
Linear Regression
 
Logistic Regression
 
Generalized Linear Regression
 
Non-linear Regression
 
 
Clustering
 
 
K-means Clustering
 
Hierarchical Clustering
 
Density-based Clustering
 
 
Outlier Detection
 
Time Series Analysis
 
 
Time Series Decomposition
 
Time Series Forecast
 
 
Association Rules
 
Sequential Patterns
 
Text Mining
 
Social Network Analysis
 
 
Case Studies
 
 
Case Study I: Analysis and Forecasting of House Price Indices
 
 
Reading Data from a CSV File
 
Data Exploration
 
Time Series Decomposition
 
Time Series Forecasting
 
Discussion
 
 
Case Study II: Customer Response Prediction
 
Case Study III: Risk Rating using Decision Tree with Limited Resources
 
Customer Behaviour Prediction and Intervention
 
 
Appendix
 
 
Online Resources
 
R Reference Card for Data Mining
 
 
Bibliography

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