R and Data Mining: Examples and Case Studies

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Academic Press, Dec 31, 2012 - Mathematics - 256 pages
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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.

  • 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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Contents

Introduction
1
Data Import and Export
5
Data Exploration
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
General Index
229
Package Index
231

Association Rules
89
Text Mining
105
Social Network Analysis
123

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About the author (2012)

A Senior Data Mining Analyst in Australia Government since 2009.

Before joining public sector, he was an Australian Postdoctoral Fellow (Industry) in the Faculty of Engineering & Information Technology at University of Technology, Sydney, Australia. His research interests include clustering, association rules, time series, outlier detection and data mining applications and he has over forty papers published in journals and conference proceedings. He is a member of the IEEE and a member of the Institute of Analytics Professionals of Australia, and served as program committee member for more than thirty international conferences.

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