Data Mining with R

Provides a self-contained introduction to the use of R for exploratory data mining and machine learning. Employing a practical, learn-by-doing approach, this work presents a series of representative case studies from ecology, financial prediction, fraud detection, and bioinformatics, including all of the necessary steps, code, and data.

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The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining 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 analysis, 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, code, 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 functions.