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R and Data Mining
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.
- 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
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
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
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