Data Mining Applications with R

Data Mining Applications with R

1st Edition - November 26, 2013

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  • Authors: Yanchang Zhao, Yonghua Cen
  • eBook ISBN: 9780124115200

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Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool.   R code, Data and color figures for the book are provided at the website.

Key Features

  • Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries
  • Presents various case studies in real-world applications, which will help readers to apply the techniques in their work
  • Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves


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. Government agencies, banks, insurance, retail, telecom, medicine and scientific research.

Table of Contents

  • Preface


    Objectives and Significance

    Target Audience


    Review Committee

    Additional Reviewers



    Chapter 1. Power Grid Data Analysis with R and Hadoop


    1.1 Introduction

    1.2 A Brief Overview of the Power Grid

    1.3 Introduction to MapReduce, Hadoop, and RHIPE

    1.4 Power Grid Analytical Approach

    1.5 Discussion and Conclusions



    Chapter 2. Picturing Bayesian Classifiers: A Visual Data Mining Approach to Parameters Optimization



    2.1 Introduction

    2.2 Related Works

    2.3 Motivations and Requirements

    2.4 Probabilistic Framework of NB Classifiers

    2.5 Two-Dimensional Visualization System

    2.6 A Case Study: Text Classification

    2.7 Conclusions


    Chapter 3. Discovery of Emergent Issues and Controversies in Anthropology Using Text Mining, Topic Modeling, and Social Network Analysis of Microblog Content


    3.1 Introduction

    3.2 How Many Messages and How Many Twitter-Users in the Sample?

    3.3 Who Is Writing All These Twitter Messages?

    3.4 Who Are the Influential Twitter-Users in This Sample?

    3.5 What Is the Community Structure of These Twitter-Users?

    3.6 What Were Twitter-Users Writing About During the Meeting?

    3.7 What Do the Twitter Messages Reveal About the Opinions of Their Authors?

    3.8 What Can Be Discovered in the Less Frequently Used Words in the Sample?

    3.9 What Are the Topics That Can Be Algorithmically Discovered in This Sample?

    3.10 Conclusion


    Chapter 4. Text Mining and Network Analysis of Digital Libraries in R


    4.1 Introduction

    4.2 Dataset Preparation

    4.3 Manipulating the Document-Term Matrix

    4.4 Clustering Content by Topics Using the LDA

    4.5 Using Similarity Between Documents to Explore Document Cohesion

    4.6 Social Network Analysis of Authors

    4.7 Conclusion


    Chapter 5. Recommender Systems in R


    5.1 Introduction

    5.2 Business Case

    5.3 Evaluation

    5.4 Collaborative Filtering Methods

    5.5 Latent Factor Collaborative Filtering

    5.6 Simplified Approach

    5.7 Roll Your Own

    5.8 Final Thoughts


    Chapter 6. Response Modeling in Direct Marketing: A Data Mining-Based Approach for Target Selection


    6.1 Introduction/Background

    6.2 Business Problem

    6.3 Proposed Response Model

    6.4 Modeling Detail

    6.5 Prediction Result

    6.6 Model Evaluation

    6.7 Conclusion


    Chapter 7. Caravan Insurance Customer Profile Modeling with R


    7.1 Introduction

    7.2 Data Description and Initial Exploratory Data Analysis

    7.3 Classifier Models of Caravan Insurance Holders

    7.4 Discussion of Results and Conclusion

    Appendix A Details of the Full Data Set Variables

    Appendix B Customer Profile Data-Frequency of Binary Values

    Appendix C Proportion of Caravan Insurance Holders vis-à-vis other Customer Profile Variables

    Appendix D LR Model Details

    Appendix E R Commands for Computation of ROC Curves for Each Model Using Validation Dataset

    Appendix F Commands for Cross-Validation Analysis of Classifier Models


    Chapter 8. Selecting Best Features for Predicting Bank Loan Default


    8.1 Introduction

    8.2 Business Problem

    8.3 Data Extraction

    8.4 Data Exploration and Preparation

    8.5 Missing Imputation

    8.6 Modeling

    8.7 Model Evaluation

    8.8 Finding and Model Deployment

    8.9 Lessons and Discussions

    Appendix Selecting Best Features for Predicting Bank Loan Default


    Chapter 9. A Choquet Integral Toolbox and Its Application in Customer Preference Analysis


    9.1 Introduction

    9.2 Background

    9.3 Rfmtool Package

    9.4 Case Study

    9.5 Conclusions


    Chapter 10. A Real-Time Property Value Index Based on Web Data



    10.1 Introduction

    10.2 Housing Prices and Indices

    10.3 A Data Mining Approach

    10.4 Real Estate Pricing Models

    10.5 Conclusion


    Chapter 11. Predicting Seabed Hardness Using Random Forest in R



    11.1 Introduction

    11.2 Study Region and Data Processing

    11.3 Dataset Manipulation and Exploratory Analyses

    11.4 Application of RF for Predicting Seabed Hardness

    11.5 Model Validation Using rfcv

    11.6 Optimal Predictive Model

    11.7 Application of the Optimal Predictive Model

    11.8 Discussion and Conclusions

    Appendix AA Dataset of Seabed Hardness and 15 Predictors

    Appendix BA R Function,, Shows the Cross-Validated Prediction Performance of a Predictive Model


    Chapter 12. Supervised Classification of Images, Applied to Plankton Samples Using R and Zooimage



    12.1 Background

    12.2 Challenges

    12.3 Data Extraction and Exploration

    12.4 Data Preprocessing

    12.5 Modeling

    12.6 Model Evaluation

    12.7 Model Deployment

    12.8 Lessons, Discussion, and Conclusions


    Chapter 13. Crime Analyses Using R


    13.1 Introduction

    13.2 Problem Definition

    13.3 Data Extraction

    13.4 Data Exploration and Preprocessing

    13.5 Visualizations

    13.6 Modeling

    13.7 Model Evaluation

    13.8 Discussions and Improvements


    Chapter 14. Football Mining with R



    14.1 Introduction to the Case Study and Organization of the Analysis

    14.2 Background of the Analysis: The Italian Football Championship

    14.3 Data Extraction and Exploration

    14.4 Data Preprocessing

    14.5 Model Development: Building Classifiers

    14.6 Model Deployment

    14.7 Concluding Remarks


    Chapter 15. Analyzing Internet DNS(SEC) Traffic with R for Resolving Platform Optimization


    15.1 Introduction

    15.2 Data Extraction from PCAP to CSV File

    15.3 Data Importation from CSV File to R

    15.4 Dimension Reduction Via PCA

    15.5 Initial Data Exploration Via Graphs

    15.6 Variables Scaling and Samples Selection

    15.7 Clustering for Segmenting the FQDN

    15.8 Building Routing Table Thanks to Clustering

    15.9 Building Routing Table Thanks to Mixed Integer Linear Programming

    15.10 Building Routing Table Via a Heuristic

    15.11 Final Evaluation

    15.12 Conclusion



Product details

  • No. of pages: 514
  • Language: English
  • Copyright: © Academic Press 2013
  • Published: November 26, 2013
  • Imprint: Academic Press
  • eBook ISBN: 9780124115200

About the Authors

Yanchang Zhao

A Senior Data Mining Analyst in Australia Government since 2009.

Before joining public sector, he was an Australian Postdoctoral Fellow (Industry) in the Faculty of Engineering & Information Technology at University of Technology, Sydney, Australia. His research interests include clustering, association rules, time series, outlier detection and data mining applications and he has over forty papers published in journals and conference proceedings. He is a member of the IEEE and a member of the Institute of Analytics Professionals of Australia, and served as program committee member for more than thirty international conferences.

Affiliations and Expertise

Senior Data Mining Specialist, Australia

Yonghua Cen

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  • CarlosRios M. Fri Apr 05 2019

    R is ok for data miners

    As a data mining beginner, I was looking for the right tool offering both variety and specificity. Data mining applications with R showed me how the various R techniques and methodologies can be used in a broad number of cases without losing efficiency and performance.