R and Data Mining

Examples and Case Studies

Inbunden, Engelska, 2013

AvYanchang Zhao,Australia) Zhao, Yanchang, PhD (Senior Data Mining Specialist,Yangchang Zhao

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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.

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

Produktinformation

Utgivningsdatum2013-01-31
Mått152 x 229 x 22 mm
Vikt570 g
FormatInbunden
SpråkEngelska
Antal sidor256
FörlagElsevier Science
ISBN9780123969637

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Programspråk inom Data och IT
Databaser inom Data och IT

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