R and Data Mining - 1st Edition - ISBN: 9780123969637, 9780123972712

R and Data Mining

1st Edition

Examples and Case Studies

Authors: Yanchang Zhao
Hardcover ISBN: 9780123969637
eBook ISBN: 9780123972712
Imprint: Academic Press
Published Date: 11th December 2012
Page Count: 256
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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.

Key Features

  • 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


Researchers in academia and industry working in the field of data mining, postgraduate students who are interested in data mining, as well as data miners and analysts from industry. Since data mining techniques are widely used in government agencies, banks, insurance, retail, telecom, medicine and research, the book will be interesting to many areas.

Table of Contents


List of Figures

List of Abbreviations

Chapter 1. Introduction

1.1 Data Mining

1.2 R

1.3 Datasets


Chapter 2. Data Import and Export

2.1 Save and Load R Data

2.2 Import from and Export to .CSV Files

2.3 Import Data from SAS

2.4 Import/Export via ODBC


Chapter 3. Data Exploration

3.1 Have a Look at Data

3.2 Explore Individual Variables

3.3 Explore Multiple Variables

3.4 More Explorations

3.5 Save Charts into Files


Chapter 4. Decision Trees and Random Forest

4.1 Decision Trees with Package party

4.2 Decision Trees with Package rpart

4.3 Random Forest


Chapter 5. Regression

5.1 Linear Regression

5.2 Logistic Regression

5.3 Generalized Linear Regression

5.4 Non-Linear Regression

Chapter 6. Clustering

6.1 The k-Means Clustering

6.2 The k-Medoids Clustering

6.3 Hierarchical Clustering

6.4 Density-Based Clustering


Chapter 7. Outlier Detection

7.1 Univariate Outlier Detection

7.2 Outlier Detection with LOF

7.3 Outlier Detection by Clustering

7.4 Outlier Detection from Time Series

7.5 Discussions


Chapter 8. Time Series Analysis and Mining

8.1 Time Series Data in R

8.2 Time Series Decomposition

8.3 Time Series Forecasting

8.4 Time Series Clustering

8.5 Time Series Classification

8.6 Discussions

8.7 Further Readings


Chapter 9. Association Rules

9.1 Basics of Association Rules

9.2 The Titanic Dataset

9.3 Association Rule Mining

9.4 Removing Redundancy

9.5 Interpreting Rules

9.6 Visualizing Association Rules

9.7 Discussions and Further Readings


Chapter 10. Text Mining

10.1 Retrieving Text from Twitter

10.2 Transforming Text

10.3 Stemming Words

10.4 Building a Term-Document Matrix

10.5 Frequent Terms and Associations

10.6 Word Cloud

10.7 Clustering Words

10.8 Clustering Tweets

10.9 Packages, Further Readings, and Discussions


Chapter 11. Social Network Analysis

11.1 Network of Terms

11.2 Network of Tweets

11.3 Two-Mode Network

11.4 Discussions and Further Readings


Chapter 12. Case Study I: Analysis and Forecasting of House Price Indices

12.1 Importing HPI Data

12.2 Exploration of HPI Data

12.3 Trend and Seasonal Components of HPI

12.4 HPI Forecasting

12.5 The Estimated Price of a Property

12.6 Discussion

Chapter 13. Case Study II: Customer Response Prediction and Profit Optimization

13.1 Introduction

13.2 The Data of KDD Cup 1998

13.3 Data Exploration

13.4 Training Decision Trees

13.5 Model Evaluation

13.6 Selecting the Best Tree

13.7 Scoring

13.8 Discussions and Conclusions


Chapter 14. Case Study III: Predictive Modeling of Big Data with Limited Memory

14.1 Introduction

14.2 Methodology

14.3 Data and Variables

14.4 Random Forest

14.5 Memory Issue

14.6 Train Models on Sample Data

14.7 Build Models with Selected Variables

14.8 Scoring

14.9 Print Rules

14.10 Conclusions and Discussion

Chapter 15. Online Resources

15.1 R Reference Cards

15.2 R

15.3 Data Mining

15.4 Data Mining with R

15.5 Classification/Prediction with R

15.6 Time Series Analysis with R

15.7 Association Rule Mining with R

15.8 Spatial Data Analysis with R

15.9 Text Mining with R

15.10 Social Network Analysis with R

15.11 Data Cleansing and Transformation with R

15.12 Big Data and Parallel Computing with R

R Reference Card for Data Mining


General Index

Package Index

Function Index


No. of pages:
© Academic Press 2012
Academic Press
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About the Author

Yanchang Zhao

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.

Affiliations and Expertise

Senior Data Mining Specialist, Australia