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R and Data Mining : Examples and Case Studies,9780123969637
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R and Data Mining : Examples and Case Studies

by
ISBN13:

9780123969637

ISBN10:
0123969638
Format:
Hardcover
Pub. Date:
12/20/2012
Publisher(s):
Academic Pr
List Price: $79.95

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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.


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