R and Data Mining: Examples and Case StudiesR and Data Mining introduces researchers, postgraduate 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 indepth 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.

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Contents
1  
5  
11  
Decision Trees and Random Forest  27 
Regression  41 
Clustering  51 
Outlier Detection  63 
Time Series Analysis and Mining  75 
Analysis and Forecasting of House Price Indices  137 
Customer Response Prediction and Profit Optimization  151 
Predictive Modeling of Big Data with Limited Memory  181 
Online Resources  213 
R Reference Card for Data Mining  221 
Bibliography  225 
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231  