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Data Mining with R: Learning with Case Studies
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Data Mining with R: Learning with Case Studies [Print Replica] [Format Kindle]

Luis Torgo

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Descriptions du produit

Présentation de l'éditeur

This hands-on book uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, it covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. The main data mining processes and techniques are presented through detailed, real-world case studies. With these case studies, the author supplies all necessary steps, code, and data. Mirroring the do-it-yourself approach of the text, the supporting website provides data sets and R code.

Biographie de l'auteur

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.

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Amazon.com: 4.2 étoiles sur 5  18 commentaires
104 internautes sur 106 ont trouvé ce commentaire utile 
3.0 étoiles sur 5 Taken over by competition 10 décembre 2010
Par Dimitri Shvorob - Publié sur Amazon.com
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 internautes sur 29 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Excellent guide with real world case studies 5 octobre 2011
Par Ravi Aranke - Publié sur Amazon.com
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)
26 internautes sur 27 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Invaluable resource for data miners 19 juin 2011
Par Sandro Saitta - Publié sur Amazon.com
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!
6 internautes sur 6 ont trouvé ce commentaire utile 
1.0 étoiles sur 5 Doesn't live up to its promise 18 juin 2014
Par Barbara - Publié sur Amazon.com
An unsatisfying sort of text book. It showed promise because it uses real data from relevant contexts to run through its examples. But it runs through the data mining examples without pausing to explain in more than the barest superficiality how the modelling and analysis methods demonstrated work, the logic behind them, or the assumptions they make about the data. I wouldn't have followed the random forest application if I hadn't already learned about random forests from a much better book, Introduction to Statistical Learning in R (available as a free PDF from the authors' website), and I learned nothing about artificial neural networks from the neural network example. To compund this, the authors make heavy use of their own functions (which you can download along with the book) instead of taking the time to show you how to achieve the same results using standard R tools and packages.
8 internautes sur 9 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 A great collection of case studies involing data mining with R 17 décembre 2011
Par Oscar Cassetti - Publié sur Amazon.com
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.
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