Data Mining with R: Learning with Case Studies (Chapman and Hall CRC Data Mining and... at Dominant Systems Book Store

If you want to learn how to analyze your data with a free software package that has been built by expert statisticians and data miners, this is your book. A broad range of real-world case studies highlights the breadth and depth of the R software.
—Bernhard Pfahringer, University of Waikato, New Zealand

Both R novices and experts will find this a great reference for data mining.
—Intelligent Trading blog and R-bloggers, November 2010

Customer Reviews & Comments
An appealing book, and the only one I know to combine "data mining" and "R" subjects. The author's command of both is not in doubt, and his writing is spry and lucid. A reader who retraces the four case studies will not get lost at any point of the trek, and complete it with useful new skills. Why not five stars then? I did not enjoy the finance-themed chapter (I am guessing the publishers did, and bumped the price up by $20), the longest yet with the least to tell about data mining, and, more broadly, felt that too much space was given to diversions and "infrastructure" code, at the expense of actual data mining. The list of methods covered in the book looks impressive - regression trees (implemented in R package rpart), random forests (randomForest), multilayer perceptrons (nnet), support vector machines (e1071, kernlab), naive Bayes (e1071), multivariate adaptive regression splines (earth), boosting (RWeka) - but when you start counting how many pages actually discuss any particular method (low single digits; MARS get just one), a disappointment sets in that's not easily dispersed by the repeated ROC-curve-assisted horse-races of different methods. I wish some editorial choices were made differently, but still appreciate the book as it is.

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