Data Mining with R: Learning with Case Studies

Data Mining with R: Learning with Case Studies Front Cover
7 Reviews
305 pages

Book Description

The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive tools. Exploring this area from the perspective of a practitioner, Data Mining with R: Learning with Case Studies uses practical examples to illustrate the power of R and data mining.

Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of , and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies:

  • Predicting algae blooms
  • Predicting stock market returns
  • Detecting fraudulent transactions
  • Classifying microarray samples

With these case studies, the author supplies all necessary steps, , and data.

Web Resource
A supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several .

Book Details

  • Title: Data Mining with R: Learning with Case Studies
  • Author:
  • Length: 305 pages
  • Edition: 1
  • Language: English
  • Publisher:
  • Publication Date: 2010-11-09
  • ISBN-10: 1439810184
  • ISBN-13: 9781439810187
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