Guides R users into data mining and helps data miners to learn to use R in their work
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
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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