9780123969637-0123969638-R and Data Mining: Examples and Case Studies

R and Data Mining: Examples and Case Studies

ISBN-13: 9780123969637
ISBN-10: 0123969638
Edition: 1
Author: Yanchang Zhao
Publication date: 2012
Publisher: Academic Press
Format: Hardcover 256 pages
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Book details

ISBN-13: 9780123969637
ISBN-10: 0123969638
Edition: 1
Author: Yanchang Zhao
Publication date: 2012
Publisher: Academic Press
Format: Hardcover 256 pages

Summary

R and Data Mining: Examples and Case Studies (ISBN-13: 9780123969637 and ISBN-10: 0123969638), written by authors Yanchang Zhao, was published by Academic Press in 2012. With an overall rating of 3.5 stars, it's a notable title among other Applied (Mathematics) books. You can easily purchase or rent R and Data Mining: Examples and Case Studies (Hardcover) from BooksRun, along with many other new and used Applied books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.59.

Description

This book guides R users into data mining and helps data miners who use R in their work. It provides a how-to method using R for data mining applications from academia to industry. It

  • Presents an introduction into using R for data mining applications, covering most popular data mining techniques
  • Provides code examples and data so that readers can easily learn the techniques
  • Features case studies in real-world applications to help readers apply the techniques in their work and studies
The R code and data for the book are provided at the RDataMining.com website.

The book helps researchers in the field of data mining, postgraduate students who are interested in data mining, and data miners and analysts from industry. For the many universities that have courses on data mining, this book is an invaluable reference for students studying data mining and its related subjects. In addition, it is a useful resource for anyone involved in industrial training courses on data mining and analytics. The concepts in this book help readers as R becomes increasingly popular for data mining applications.
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