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Informationen zum Autor 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. Klappentext An introduction to using R for data mining applications, covering the most popular techniques. Zusammenfassung Introduces researchers! post-graduate students! and analysts to data mining using R! a free software environment for statistical computing and graphics. This book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data.
List of contents
- 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
