Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

• Covers the main data mining techniques through carefully selected case studies
• Describes code and approaches that can be easily reproduced or adapted to your own problems
• Requires no prior experience with R
• Includes introductions to R and MySQL basics
• Provides a fundamental understanding of the merits, drawbacks, and analysis objectives of the data mining techniques
• Offers data and R code on www.liaad.up.pt/~ltorgo/DataMiningWithR/

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:

1. Predicting algae blooms
2. Predicting stock market returns
3. Detecting fraudulent transactions
4. 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.

Luis Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.