Data mining with R : learning with case studies / Luís Torgo.

Format:

Book

Author/Creator:

Torgo, Luís.

Contributor:

Christine Hikawa Fund.

Series:

Chapman & Hall/CRC data mining and knowledge discovery series

Language:

English

Subjects (All):

Data mining--Case studies.

Data mining.

R (Computer program language).

Genre:

Case studies.

Physical Description:

xv, 289 pages : illustrations ; 25 cm.

Place of Publication:

Boca Raton, FL : Chapman & Hall/CRC, [2011]

Summary:

"The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data mining with R: learning with case studies uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies: predicting algae blooms, predicting stock market returns, detecting fraudulent transactions, classifying microarray samples. With these case studies, the author supplies all necessary steps, code, and data. Resource: A supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions"-- Provided by publisher.

"This hands-on book uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, it covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. The main data mining processes and techniques are presented through detailed, real-world case studies. With these case studies, the author supplies all necessary steps, code, and data. Mirroring the do-it-yourself approach of the text, the supporting website provides data sets and R code"-- Provided by publisher.

Contents:

1 Introduction 1

1.1 How to Read This Book? 2

1.2 A Short Introduction to R 3

1.2.1 Starting with R 3

1.2.2 R Objects 5

1.2.3 Vectors 7

1.2.4 Vectorization 10

1.2.5 Factors 11

1.2.6 Generating Sequences 14

1.2.7 Sub-Setting 16

1.2.8 Matrices and Arrays 19

1.2.9 Lists 23

1.2.10 Data Frames 26

1.2.11 Creating New Functions 30

1.2.12 Objects, Classes, and Methods 33

1.2.13 Managing Your Sessions 34

1.3 A Short Introduction to MySQL 35

2 Predicting Algae Blooms 39

2.1 Problem Description and Objectives 39

2.2 Data Description 40

2.3 Loading the Data into R 41

2.4 Data Visualization and Summarization 43

2.5 Unknown Values 52

2.5.1 Removing the Observations with Unknown Values 53

2.5.2 Filling in the Unknowns with the Most Frequent Values 55

2.5.3 Filling in the Unknown Values by Exploring Correlations 56

2.5.4 Filling in the Unknown Values by Exploring Similarities between Cases 60

2.6 Obtaining Prediction Models 63

2.6.1 Multiple Linear Regression 64

2.6.2 Regression Trees 71

2.7 Model Evaluation and Selection 77

2.8 Predictions for the Seven Algae 91

2.9 Summary 94

3 Predicting Stock Market Returns 95

3.1 Problem Description and Objectives 95

3.2 The Available Data 96

3.2.1 Handling Time-Dependent Data in R 97

3.2.2 Reading the Data from the CSV File 101

3.2.3 Getting the Data from the Web 102

3.2.4 Reading the Data from a MySQL Database 104

3.2.4.1 Loading the Data into R Running on Windows 105

3.2.4.2 Loading the Data into R Running on Linux 107

3.3 Defining the Prediction Tasks 108

3.3.1 What to Predict? 108

3.3.2 Which Predictors? 111

3.3.3 The Prediction Tasks 117

3.3.4 Evaluation Criteria 118

3.4 The Prediction Models 120

3.4.1 How Will the Training Data Be Used? 121

3.4.2 The Modeling Tools 123

3.4.2.1 Artificial Neural Networks 123

3.4.2.2 Support Vector Machines 126

3.4.2.3 Multivariate Adaptive Regression Splines 129

3.5 From Predictions into Actions 130

3.5.1 How Will the Predictions Be Used? 130

3.5.2 Trading-Related Evaluation Criteria 132

3.5.3 Putting Everything Together: A Simulated Trader 133

3.6 Model Evaluation and Selection 141

3.6.1 Monte Carlo Estimates 141

3.6.2 Experimental Comparisons 143

3.6.3 Results Analysis 148

3.7 The Trading System 156

3.7.1 Evaluation of the Final Test Data 156

3.7.2 An Online Trading System 162

3.8 Summary 163

4 Detecting Fraudulent Transactions 165

4.1 Problem Description and Objectives 165

4.2 The Available Data 166

4.2.1 Loading the Data into R 166

4.2.2 Exploring the Dataset 167

4.2.3 Data Problems 174

4.2.3.1 Unknown Values 175

4.2.3.2 Few Transactions of Some Products 179

4.3 Defining the Data Mining Tasks 183

4.3.1 Different Approaches to the Problem 183

4.3.1.1 Unsupervised Techniques 184

4.3.1.2 Supervised Techniques 185

4.3.1.3 Semi-Supervised Techniques 186

4.3.2 Evaluation Criteria 187

4.3.2.1 Precision and Recall 188

4.3.2.2 Lift Charts and Precision/Recall Curves 188

4.3.2.3 Normalized Distance to Typical Price 193

4.3.3 Experimental Methodology 194

4.4 Obtaining Outlier Rankings 195

4.4.1 Unsupervised Approaches 196

4.4.1.1 The Modified Box Plot Rule 196

4.4.1.2 Local Outlier Factors (LOF) 201

4.4.1.3 Clustering-Based Outlier Rankings (OR_h) 205

4.4.2 Supervised Approaches 208

4.4.2.1 The Class Imbalance Problem 209

4.4.2.2 Naive Bayes 211

4.4.2.3 AdaBoost 217

4.4.3 Semi-Supervised Approaches 223

4.5 Summary 230

5 Classifying Microarray Samples 233

5.1 Problem Description and Objectives 233

5.1.1 Brief Background on Microarray Experiments 233

5.1.2 The ALL Dataset 234

5.2 The Available Data 235

5.2.1 Exploring the Dataset 238

5.3 Gene (Feature) Selection 241

5.3.1 Simple Filters Based on Distribution Properties 241

5.3.2 ANOVA Filters 244

5.3.3 Filtering Using Random Forests 246

5.3.4 Filtering Using Feature Clustering Ensembles 248

5.4 Predicting Cytogenetic Abnormalities 251

5.4.1 Defining the Prediction Task 251

5.4.2 The Evaluation Metric 252

5.4.3 The Experimental Procedure 253

5.4.4 The Modeling Techniques 254

5.4.4.1 Random Forests 254

5.4.4.2 k-Nearest Neighbors 255

5.4.5 Comparing the Models 258

5.5 Summary 267.

Notes:

Includes bibliographical references and indexes.

Local Notes:

Acquired for the Penn Libraries with assistance from the Christine Hikawa Fund.

ISBN:

1439810184

9781439810187

OCLC:

665064348

Publisher Number:

99953810968