Data Mining Applications with R

  • ID: 2496350
  • Book
  • 514 Pages
  • Elsevier Science and Technology
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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.

- 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
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1. Introduction

2. Case Study in Finance

3.Case Study in Retail

4. Case Study in Telecommunications

5. Case Study in Government

6. Case Study in Crime & Homeland Security

7. Case Study in Stock Market

8. Case Study in Social Welfare

9. Case Study in Social Media

10. Case Study in Sports

11. Case Study in Medicine and Health

12. Case Study in Bioinformatics

13. Case Study in Sentiment Analysis

14. Case Study in Spatial Data Analysis

15. Case Study in Patent Analysis

16. Case Study in Education

17. Case Study in Transport

18. Case Study in Real Estate



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Zhao, Yanchang
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.
Cen, Yonghua

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