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Data Mining with R (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Data Mining with R (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) [Kindle Edition]

Torgo , Luis

Kindle-Preis: EUR 46,51 Inkl. MwSt. und kostenloser drahtloser Lieferung über Amazon Whispernet

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Kindle Edition, 11. September 2010 EUR 46,51  
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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


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.


  • Format: Kindle Edition
  • Dateigröße: 7870 KB
  • Seitenzahl der Print-Ausgabe: 305 Seiten
  • Verlag: Chapman & Hall; Auflage: 1 (11. September 2010)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Amazon Bestseller-Rang: #307.673 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)

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Die hilfreichsten Kundenrezensionen auf (beta) 4.1 von 5 Sternen  17 Rezensionen
103 von 105 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen Taken over by competition 10. Dezember 2010
Von Dimitri Shvorob - Veröffentlicht auf
Format:Gebundene Ausgabe
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.
29 von 29 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Excellent guide with real world case studies 5. Oktober 2011
Von Ravi Aranke - Veröffentlicht auf
Format:Gebundene Ausgabe
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 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 (
26 von 27 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Invaluable resource for data miners 19. Juni 2011
Von Sandro Saitta - Veröffentlicht auf
Format:Gebundene Ausgabe
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!
7 von 8 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A great collection of case studies involing data mining with R 17. Dezember 2011
Von Oscar Cassetti - Veröffentlicht auf
Format:Gebundene Ausgabe
This is a really nice collection of case studies involving data mining with R. Both supervised and unsupervised methods are presented. The book is quite technical with big chunks of R code. However it is not a book about data mining or R. You will need other books such as Introduction to Data Mining Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) The R Book to cover these topics.
5 von 6 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Great introduction to R and Data Mining 22. April 2012
Von asenski - Veröffentlicht auf
Format:Gebundene Ausgabe|Verifizierter Kauf
I recently completed the free online machine learning class by professor Andrew Ng and became a lot more interested in the field. The class was done in Octave (which is an open source language close to MatLab). However I was curious what R was capable of doing and wanted to find a book that would make the analogies easier for me.

This book did the job perfectly as it captures both R and data mining, and even though some may argue it is at a somewhat basic level, I think for people looking to transition into R, this is the best guide they will find.

I love learning new languages using a basic step practical examples. This book will not teach you the most complicated techniques used in data mining, but I never expected it to. I just wanted to know what to use to import the data, run analysis, visualize various aspects of it and then export or apply results.

Again for people coming from MatLab or Octave, this is a great book! (worked great for me)
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Beliebte Markierungen

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The result of this cor() function is not very legible but we can put it through the function symnum() to improve this: &quote;
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The R package PerformanceAnalytics (Carl and Peterson, 2009) implements many of the existing financial metrics for analyzing the returns of &quote;
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As you can confirm in the examples, separators can be omitted and parts of the time specification left out to include sets of time tags. Moreover, the / symbol can be used to specify time intervals that can unspecified on both ends, with the meaning of start or final time &quote;
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