R and Data Mining

Examples and Case Studies

Yanchang Zhao

• Publisher: Academic Press
• Year: 2012
• Print ISBN: 9780123969637
• eBook ISBN: 9780123972712
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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

Chapter 1

Introduction

This book introduces into using R for data mining. It presents many examples of various data mining functionalities in R and three case studies of real-world applications. The supposed audience of this book are postgraduate students, researchers, and data miners who are interested in using R to do their data mining research and projects. We assume that readers already have a basic idea of data mining and also have some basic experience with R. We hope that this book will encourage more and more people to use R to do data mining work in their research and applications.

This chapter introduces basic concepts and techniques for data mining, including a data mining process and popular data mining techniques. It also presents R and its packages, functions, and task views for data mining. At last, some datasets used in this book are described.

1.1 Data Mining

Data mining is the process to discover interesting knowledge from large amounts of data (Han and Kamber, 2000). It is an interdisciplinary field with contributions from many areas, such as statistics, machine learning, information retrieval, pattern recognition, and bioinformatics. Data mining is widely used in many domains, such as retail, finance, telecommunication, and social media.

The main techniques for data mining include classification and prediction, clustering, outlier detection, association rules, sequence analysis, time series analysis, and text mining, and also some new techniques such as social network analysis and sentiment analysis. Detailed introduction of data mining techniques can be found in text books on data mining (Han and Kamber, 2000; Hand et al., 2001; Witten and Frank, 2005). In real-world applications, a data mining process can be broken into six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment, as defined by the CRISP-DM (Cross Industry Standard Process for Data Mining).¹ This book focuses on the modeling phase, with data exploration and model evaluation involved in some chapters. Readers who want more information on data mining are referred to online resources in Chapter 15.

1.2 R

R² (R Development Core Team, 2012) is a free software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques. R can be extended easily via packages. There are around 4000 packages available in the CRAN package repository,³ as on August 1, 2012. More details about R are available in An Introduction to R⁴ (Venables et al., 2010) and R Language Definition⁵ (R Development Core Team, 2010b) at the CRAN website. R is widely used in both academia and industry.

To help users to find out which R packages to use, the CRAN Task Views⁶ are a good guidance. They provide collections of packages for different tasks. Some task views related to data mining are:

• Machine Learning and Statistical Learning;

• Cluster Analysis and Finite Mixture Models;

• Time Series Analysis;

• Multivariate Statistics; and

• Analysis of Spatial Data.

Another guide to R for data mining is an R Reference Card for Data Mining (see p. 221), which provides a comprehensive indexing of R packages and functions for data mining, categorized by their functionalities. Its latest version is available at http://www.rdatamining.com/docs.

Readers who want more information on R are referred to online resources in Chapter 15.

1.3 Datasets

The datasets used in this book are briefly described in this section.

1.3.1 The Iris Dataset

The iris dataset has been used for classification in many research publications. It consists of 50 samples from each of three classes of iris flowers (Frank and Asuncion, 2010). One class is linearly separable from the other two, while the latter are not linearly separable from each other. There are five attributes in the dataset:

• sepal length in cm,

• sepal width in cm,

• petal length in cm,

• petal width in cm, and

• class: Iris Setosa, Iris Versicolour, and Iris Virginica.

> str(iris)

‘data.frame’: 150 obs.of 5 variables:

$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 …

$ Sepal.Width: num 3.5 ...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• List of Figures
• List of Abbreviations
• Chapter 1. Introduction
• Chapter 2. Data Import and Export
• Chapter 3. Data Exploration
• Chapter 4. Decision Trees and Random Forest
• Chapter 5. Regression
• Chapter 6. Clustering
• Chapter 7. Outlier Detection
• Chapter 8. Time Series Analysis and Mining
• Chapter 9. Association Rules
• Chapter 10. Text Mining
• Chapter 11. Social Network Analysis
• Chapter 12. Case Study I: Analysis and Forecasting of House Price Indices
• Chapter 13. Case Study II: Customer Response Prediction and Profit Optimization
• Chapter 14. Case Study III: Predictive Modeling of Big Data with Limited Memory
• Chapter 15. Online Resources
• R Reference Card for Data Mining
• Bibliography
• General Index
• Package Index
• Function Index