R and Data Mining
Examples and Case Studies
Published on 31. January 2013
Book
Hardback
256 pages
978-0-12-396963-7 (ISBN)
Description
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.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
College/higher education
Professional and scholarly
Researchers in academia and industry working in the field of data mining, postgraduate students who are interested in data mining, as well as data miners and analysts from industry. Since data mining techniques are widely used in government agencies, banks, insurance, retail, telecom, medicine and research, the book will be interesting to many areas.
Product notice
Laminated cover
Dimensions
Height: 236 mm
Width: 154 mm
Thickness: 27 mm
Weight
558 gr
ISBN-13
978-0-12-396963-7 (9780123969637)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
12/2012
Academic Press
€59.95
Available for download
Person
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.
Content
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
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