Half Title -- R and Data Mining -- Copyright -- Dedication -- Contents -- List of Figures -- List of Abbreviations -- Introduction -- 1.1 Data Mining -- 1.2 R -- 1.3 Datasets -- 1.3.1 The Iris Dataset -- 1.3.2 The Bodyfat Dataset -- Data Import and Export -- 2.1 Save and Load R Data -- 2.2 Import from and Export to .CSV Files -- 2.3 Import Data from SAS -- 2.4 Import/Export via ODBC -- 2.4.1 Read from Databases -- 2.4.2 Output to and Input from EXCEL Files -- Data Exploration -- 3.1 Have a Look at Data -- 3.2 Explore Individual Variables -- 3.3 Explore Multiple Variables -- 3.4 More Explorations -- 3.5 Save Charts into Files -- Decision Trees and Random Forest -- 4.1 Decision Trees with Package party -- 4.2 Decision Trees with Package rpart -- 4.3 Random Forest -- Regression -- 5.1 Linear Regression -- 5.2 Logistic Regression -- 5.3 Generalized Linear Regression -- 5.4 Non-Linear Regression -- Clustering -- 6.1 The k-Means Clustering -- 6.2 The k-Medoids Clustering -- 6.3 Hierarchical Clustering -- 6.4 Density-Based Clustering -- Outlier Detection -- 7.1 Univariate Outlier Detection -- 7.2 Outlier Detection with LOF -- 7.3 Outlier Detection by Clustering -- 7.4 Outlier Detection from Time Series -- 7.5 Discussions -- Time Series Analysis and Mining -- 8.1 Time Series Data in R -- 8.2 Time Series Decomposition -- 8.3 Time Series Forecasting -- 8.4 Time Series Clustering -- 8.4.1 Dynamic Time Warping -- 8.4.2 Synthetic Control Chart Time Series Data -- 8.4.3 Hierarchical Clustering with Euclidean Distance -- 8.4.4 Hierarchical Clustering with DTW Distance -- 8.5 Time Series Classification -- 8.5.1 Classification with Original Data -- 8.5.2 Classification with Extracted Features -- 8.5.3 k-NN Classification -- 8.6 Discussions -- 8.7 Further Readings -- Association Rules -- 9.1 Basics of Association Rules -- 9.2 The Titanic Dataset.

9.3 Association Rule Mining -- 9.4 Removing Redundancy -- 9.5 Interpreting Rules -- 9.6 Visualizing Association Rules -- 9.7 Discussions and Further Readings -- Text Mining -- 10.1 Retrieving Text from Twitter -- 10.2 Transforming Text -- 10.3 Stemming Words -- 10.4 Building a Term-Document Matrix -- 10.5 Frequent Terms and Associations -- 10.6 Word Cloud -- 10.7 Clustering Words -- 10.8 Clustering Tweets -- 10.8.1 Clustering Tweets with the k-Means Algorithm -- 10.8.2 Clustering Tweets with the k-Medoids Algorithm -- 10.9 Packages, Further Readings, and Discussions -- Social Network Analysis -- 11.1 Network of Terms -- 11.2 Network of Tweets -- 11.3 Two-Mode Network -- 11.4 Discussions and Further Readings -- Case Study I: Analysis and Forecasting of House Price Indices -- 12.1 Importing HPI Data -- 12.2 Exploration of HPI Data -- 12.3 Trend and Seasonal Components of HPI -- 12.4 HPI Forecasting -- 12.5 The Estimated Price of a Property -- 12.6 Discussion -- Case Study II: Customer Response Prediction and Profit Optimization -- 13.1 Introduction -- 13.2 The Data of KDD Cup 1998 -- 13.3 Data Exploration -- 13.4 Training Decision Trees -- 13.5 Model Evaluation -- 13.6 Selecting the Best Tree -- 13.7 Scoring -- 13.8 Discussions and Conclusions -- Case Study III: Predictive Modeling of Big Data with Limited Memory -- 14.1 Introduction -- 14.2 Methodology -- 14.3 Data and Variables -- 14.4 Random Forest -- 14.5 Memory Issue -- 14.6 Train Models on Sample Data -- 14.7 Build Models with Selected Variables -- 14.8 Scoring -- 14.9 Print Rules -- 14.9.1 Print Rules in Text -- 14.9.2 Print Rules for Scoring with SAS -- 14.10 Conclusions and Discussion -- Online Resources -- 15.1 R Reference Cards -- 15.2 R -- 15.3 Data Mining -- 15.4 Data Mining with R -- 15.5 Classification/Prediction with R -- 15.6 Time Series Analysis with R.

15.7 Association Rule Mining with R -- 15.8 Spatial Data Analysis with R -- 15.9 Text Mining with R -- 15.10 Social Network Analysis with R -- 15.11 Data Cleansing and Transformation with R -- 15.12 Big Data and Parallel Computing with R -- R Reference Card for Data Mining -- Bibliography -- General Index -- Package Index -- Function Index.