| List of Figures | p. xi |
| List of Abbreviations | p. xv |
| Introduction | p. 1 |
| Data Mining | p. 1 |
| R | p. 2 |
| Datasets | p. 2 |
| The Iris Dataset | p. 2 |
| The Bodyfat Dataset | p. 3 |
| Data Import and Export | p. 5 |
| Save and Load R Data | p. 5 |
| Import from and Export to .CSV Files | p. 5 |
| Import Data from SAS | p. 6 |
| Import/Export via ODBC | p. 8 |
| Read from Databases | p. 8 |
| Output to and Input from EXCEL Files | p. 9 |
| Data Exploration | p. 11 |
| Have a Look at Data | p. 11 |
| Explore Individual Variables | p. 13 |
| Explore Multiple Variables | p. 16 |
| More Explorations | p. 20 |
| Save Charts into Files | p. 25 |
| Decision Trees and Random Forest | p. 27 |
| Decision Trees with Package party | p. 27 |
| Decision Trees with Package rpart | p. 31 |
| Random Forest | p. 36 |
| Regression | p. 41 |
| Linear Regression | p. 41 |
| Logistic Regression | p. 47 |
| Generalized Linear Regression | p. 48 |
| Non-Linear Regression | p. 50 |
| Clustering | p. 51 |
| The k-Means Clustering | p. 51 |
| The k-Medoids Clustering | p. 53 |
| Hierarchical Clustering | p. 56 |
| Density-Based Clustering | p. 57 |
| Outlier Detection | p. 63 |
| Univariate Outlier Detection | p. 63 |
| Outlier Detection with LOF | p. 66 |
| Outlier Detection by Clustering | p. 70 |
| Outlier Detection from Time Series | p. 72 |
| Discussions | p. 73 |
| Time Series Analysis and Mining | p. 75 |
| Time Series Data in R | p. 75 |
| Time Series Decomposition | p. 76 |
| Time Series Forecasting | p. 78 |
| Time Series Clustering | p. 78 |
| Dynamic Time Warping | p. 79 |
| Synthetic Control Chart Time Series Data | p. 79 |
| Hierarchical Clustering with Euclidean Distance | p. 80 |
| Hierarchical Clustering with DTW Distance | p. 82 |
| Time Series Classification | p. 83 |
| Classification with Original Data | p. 83 |
| Classification with Extracted Features | p. 84 |
| k-NN Classification | p. 86 |
| Discussions | p. 87 |
| Further Readings | p. 87 |
| Association Rules | p. 89 |
| Basics of Association Rules | p. 89 |
| The Titanic Dataset | p. 90 |
| Association Rule Mining | p. 92 |
| Removing Redundancy | p. 96 |
| Interpreting Rules | p. 98 |
| Visualizing Association Rules | p. 99 |
| Discussions and Further Readings | p. 103 |
| Text Mining | p. 105 |
| Retrieving Text from Twitter | p. 105 |
| Transforming Text | p. 106 |
| Stemming Words | p. 108 |
| Building a Term-Document Matrix | p. 110 |
| Frequent Terms and Associations | p. 111 |
| Word Cloud | p. 113 |
| Clustering Words | p. 114 |
| Clustering Tweets | p. 116 |
| Clustering Tweets with the k-Means Algorithm | p. 116 |
| Clustering Tweets with the k-Medoids Algorithm | p. 118 |
| Packages, Further Readings, and Discussions | p. 121 |
| Social Network Analysis | p. 123 |
| Network of Terms | p. 123 |
| Network of Tweets | p. 127 |
| Two-Mode Network | p. 132 |
| Discussions and Further Readings | p. 136 |
| Case Study I: Analysis and Forecasting of House Price Indices | p. 137 |
| Importing HPI Data | p. 137 |
| Exploration of HPI Data | p. 138 |
| Trend and Seasonal Components of HPI | p. 145 |
| HPI Forecasting | p. 147 |
| The Estimated Price of a Property | p. 149 |
| Discussion | p. 149 |
| Case Study II: Customer Response Prediction and Profit Optimization | p. 151 |
| Introduction | p. 151 |
| The Data of KDD Cup 1998 | p. 151 |
| Data Exploration | p. 160 |
| Training Decision Trees | p. 166 |
| Model Evaluation | p. 170 |
| Selecting the Best Tree | p. 173 |
| Scoring | p. 176 |
| Discussions and Conclusions | p. 179 |
| Case Study III: Predictive Modeling of Big Data with Limited Memory | p. 181 |
| Introduction | p. 181 |
| Methodology | p. 182 |
| Data and Variables | p. 182 |
| Random Forest | p. 183 |
| Memory Issue | p. 185 |
| Train Models on Sample Data | p. 186 |
| Build Models with Selected Variables | p. 188 |
| Scoring | p. 194 |
| Print Rules | p. 201 |
| Print Rules in Text | p. 201 |
| Print Rules for Scoring with SAS | p. 205 |
| Conclusions and Discussion | p. 211 |
| Online Resources | p. 213 |
| R Reference Cards | p. 213 |
| R | p. 213 |
| Data Mining | p. 214 |
| Data Mining with R | p. 216 |
| Classification/Prediction with R | p. 216 |
| Time Series Analysis with R | p. 216 |
| Association Rule Mining with R | p. 216 |
| Spatial Data Analysis with R | p. 217 |
| Text Mining with R | p. 217 |
| Social Network Analysis with R | p. 217 |
| Data Cleansing and Transformation with R | p. 218 |
| Big Data and Parallel Computing with R | p. 218 |
| R Reference Card for Data Mining | p. 221 |
| Bibliography | p. 225 |
| General Index | p. 229 |
| Package Index | p. 231 |
| Function Index | p. 233 |
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