R and Data Mining

by Zhao

• ISBN13: 9780123969637
• ISBN10: 0123969638
• Format: Hardcover
• Copyright: 2012-12-11
• Publisher: Elsevier Science

Summary

This book introduces into using R for data mining. Data mining techniques are widely used in government agencies, banks, insurance, retail, telecom, medicine and research. Recently, there is an increasing tendency to do data mining with R, a free software environment for statistical computing and graphics . According to a poll by KDnuggets.com in early 2011, R is the 2nd popular tool for data mining work. By introducing into using R for data mining, this book will have a broad audience from both academia and industry. It targets researchers in the field of data mining, postgraduate students who are interested in data mining, as well as data miners and analysts from industry. For example, many universities have courses on data mining, and the proposed book will be a useful reference for students learning data mining in those courses. There are also many training courses on data mining in industry, such as training by SAS and IBM on data mining. The book will be interested to the course learners as well. The book will present an introduction into using R for data mining applications, coving most popular data mining techniques. Code examples and data will be provided, so that readers can easily learn the techniques. Case studies in real-world applications will be covered, which will help readers to apply the techniques in their work.

Table 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

Customer Response Prediction

Risk Rating using Decision Tree with Limited Resources

Customer Behaviour Prediction and Intervention

Appendix

Online Resources

R Reference Card for Data Mining

Bibliography

Table of Contents provided by Publisher. All Rights Reserved.