R and Data Mining

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.

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