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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:
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.
- Sales Rank: #605410 in eBooks
- Published on: 2011-09-22
- Released on: 2011-09-22
- Format: Kindle eBook
Review
This is certainly one of the best books for a direct implementation of data mining algorithms. Another good point of the book is that for most of the problems there are different ways to solve them. … an invaluable resource for data miners, R programmers, as well as people involved in fields such as fraud detection and stock market prediction. If you’re serious about data mining and want to learn from experiences in the field, don’t hesitate!
―Sandro Saitta, Data Mining Research blog, May 2011
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
About the Author
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.
Most helpful customer reviews
109 of 112 people found the following review helpful.
Taken over by competition
By Dimitri Shvorob
It's January 2014 - and I am glad that better books have come out since I posted the original review, and one no longer has to accept CRC Hall's greedy pricing, and pay $65 for what really is a pretty imperfect book just because there is no choice. I'd say - pass on "Data mining with R", and go for "Introduction to statistical learning" by James, Witten, Hastie and Tibshirani if you want a high-quality, accessible R-illustrated textbook, or for "Machine learning with R" by Brett Lantz if you are eager to jump into hacking, and value code over theory.
31 of 31 people found the following review helpful.
Excellent guide with real world case studies
By Ravi Aranke
If you are on a journey to become a data scientist, do yourself a favor and pick up a copy of this book.
Since R is an open source language with a strong community, there is no dearth of information and tutorials which will help the beginner quickly get up to speed (I highly recommend 'R Cookbook' by Paul Teetor).
What was lacking, in my opinion, was a book targeted at practitioners. A book which you can pick up and start using R in your work. A book which will compress the learning curve and equip you for real world mastery - to the point where, perhaps, you might head straight to Kaggle.com and take part in data mining competitions.
The book by Luis Torgo admirably fills this gap. In the context of the case studies, the author painstakingly describes the challenges one would face in real life - such as - how to go about cleaning and munging the data, how to visualize and summarize the data, how to come up with plausible hypothesis and test them. Since data mining is as much art as science, this kind of approach where you see an expert in action and see how they go about making design choices is highly educational.
Along the tour, you also learn about several popular add-on libraries such as xts, rocr and hmisc.
Once again, an excellent how-to book and highly recommended as your 2nd R book.
Ravi Aranke (longtaildata.com)
27 of 29 people found the following review helpful.
Invaluable resource for data miners
By Sandro Saitta
The book starts with an Introduction to R. Nicely written, it explains concepts that are needed to use this programming language for data mining. The book is then divided in four case studies. Each case study introduces data mining concepts that are illustrated using R.
First, pre-processing and data visualization are introduced through the prediction of algae blooms. Second, come the modelling and time ordering with the stock market application. Then, outlier detection and clustering are presented through fraud detection. Finally, feature selection and cross-validation are introduced through the classification of microarray samples. There is no introduction to data mining, but it's not a problem since concepts are explained through the different case studies.
Theoretical concepts are always linked to examples. This is the case for most of the data mining books. Luis goes a step further by linking each application to the corresponding code in R. It is thus easy to both understand a concept as well as implementing it with R. This is certainly one of the best book for a direct implementation of data mining algorithms. Another good point of the book is that for most of the problems there are different ways to solve them.
I have one remark regarding the stock market prediction chapter. I have already discussed this issue when I was working in finance. The author states that the percentage of profitable trades should be above 50% to have a successful trading strategy. This is not always the case. Imagine a system where each winning trade brings $2 while loosing trades costs $1. Since you can earn more money with winning trades than what you loose with loosing trades, you can thus still have a successful trading strategy with 48% of winning trades, for example.
As a conclusion, this is an invaluable resource for data miners, R programmers as well as people involved in fields such as fraud detection and stock market prediction. If you're serious about data mining and want to learn from experiences in the field, don't hesitate!
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