R and Data Mining: Examples and Case Studies
Table of Contents:1 Introduction       1.1 Data Mining    1.2 R    1.3 Datasets        1.3.1 The Iris Dataset        1.3.2 The Bodyfat Dataset2 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 Files3 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 Files4 Decision Trees and Random Forest    4.1 Decision Trees with Package party    4.2 Decision Trees with Package rpart    4.3 Random Forest5 Regression    5.1 Linear Regression    5.2 Logistic Regression    5.3 Generalized Linear Regression    5.4 Non-linear Regression6 Clustering    6.1 The k-Means Clustering    6.2 The k-Medoids Clustering    6.3 Hierarchical Clustering    6.4 Density-based Clustering7 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 Discussions8 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 Readings9 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 Readings10 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 Discussions11 Social Network Analysis    11.1 Network of Terms    11.2 Network of Tweets    11.3 Two-Mode Network    11.4 Discussions and Further Readings12 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 Discussion13 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 Conclusions14 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 Discussion15 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