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 RDatasets used in this book Data Loading and Exploration Data Import/Export Save/Load R DataImport from and Export to .CSV FilesImport Data from SASImport/Export via ODBC Data Exploration Have a Look at DataExplore Individual VariablesExplore Multiple VariablesMore ExplorationSave Charts as Files Data Mining Examples Decision Trees Building Decision Trees with Package partyBuilding Decision Trees with Package rpartRandom Forest Regression Linear RegressionLogistic RegressionGeneralized Linear RegressionNon-linear Regression Clustering K-means ClusteringHierarchical ClusteringDensity-based Clustering Outlier DetectionTime Series Analysis Time Series DecompositionTime Series Forecast Association RulesSequential PatternsText MiningSocial Network Analysis Case Studies Case Study I: Analysis and Forecasting of House Price Indices Reading Data from a CSV FileData ExplorationTime Series DecompositionTime Series ForecastingDiscussion Case Study II: Customer Response PredictionCase Study III: Risk Rating using Decision Tree with Limited ResourcesCustomer Behaviour Prediction and Intervention Appendix Online ResourcesR Reference Card for Data Mining Bibliography