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R and Data Mining

Examples and Case Studies

By
  • Yanchang Zhao, Senior Data Mining Specialist, Australia

This book introduces using R for data mining. Data mining techniques are widely used in government agencies, banks, insurance, retail, telecom, medicine and research. Recently, there is an increasing tendency to do data mining with R, a free software environment for statistical computing and graphics. According to a poll by KDnuggets.com in early 2011, R is the 2 nd popular tool for data mining work. By introducing using R for data mining, this book will have a broad audience from both academia and industry. It targets researchers in the field of data mining, postgraduate students who are interested in data mining, and data miners and analysts from industry. For example, many universities have courses on data mining, and the proposed book will be a useful reference for students learning data mining in those courses. There are also many training courses on data mining in industry, such as training by SAS and IBM on data mining. The book will be of interest to the course learners as well.

Audience
Researchers in academia and industry working in the field of data mining, postgraduate students who are interested in data mining, as well as data miners and analysts from industry. Since data mining techniques are widely used in government agencies, banks, insurance, retail, telecom, medicine and research, the book will be interesting to many areas.

Hardbound, 256 Pages

Published: December 2012

Imprint: Academic Press

ISBN: 978-0-12-396963-7

Contents

    1. Introduction
      1. Introduction, Data mining
        1. R
        2. Datasets used in this book

    2. Data Loading and Exploration

      1. Data Import/Export

        1. Save/Load R Data
        2. Import from and Export to .CSV Files
        3. Import Data from SAS
        4. Import/Export via ODBC

      2. Data Exploration

        1. Have a Look at Data
        2. Explore Individual Variables
        3. Explore Multiple Variables
        4. More Exploration
        5. Save Charts as Files

    3. Data Mining Examples

      1. Decision Trees

        1. Building Decision Trees with Package party
        2. Building Decision Trees with Package rpart
        3. Random Forest

      2. Regression

        1. Linear Regression
        2. Logistic Regression
        3. Generalized Linear Regression
        4. Non-linear Regression

      3. Clustering

        1. K-means Clustering
        2. Hierarchical Clustering
        3. Density-based Clustering

      4. Outlier Detection
      5. Time Series Analysis

        1. Time Series Decomposition
        2. Time Series Forecast

      6. Association Rules
      7. Sequential Patterns
      8. Text Mining
      9. Social Network Analysis

    4. Case Studies

      1. Case Study I: Analysis and Forecasting of House Price Indices

        1. Reading Data from a CSV File
        2. Data Exploration
        3. Time Series Decomposition
        4. Time Series Forecasting
        5. Discussion

      2. Case Study II: Customer Response Prediction
      3. Case Study III: Risk Rating using Decision Tree with Limited Resources
      4. Customer Behaviour Prediction and Intervention

    5. Appendix

      1. Online Resources
      2. R Reference Card for Data Mining

    Bibliography

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