I am so happy I decided to buy this book. From the first page it is clear the author writes in excellent, approachable style -- there is no single place where I could find the author shows off his knowledge, nope, Graham Williams really teaches so the reader could understand. He allows to make friends with data mining in painless way; I appreciate the fact the first approach to each technique is done via GUI frontend (Rattle), but then the internals of R are explained -- the name of the package is given, how Rattle calls this or that function behind the scene, and also how to interpret the outcome, so in the end it is not only about the clicking with mouse.

I find this book very valuable for two reasons -- the comfort it provides (scientific book, and you want to read more and more), and it helps you focus on modeling the data, tweaking, etc (big plus for emphasis on training, validation and testing the models). However it is introductory book (I just can wish every introductory book was so informative and friendly). It is not related to the topic of data mining, but I noticed with pleasure the clear voice for open implementations of algorithms -- I can only concur that public reviews and collaboration allow to move things forward in faster pace.

If you would like to learn by example what the data mining is, how to apply, compare various algorithms this is the book for you. If you already know that and you would like to translate your knowledge to R, to some degree I would also recommend this book. This is not a book for learning R per se, so if you don't know the R it won't teach you that (rather make you a bit familiar), but don't worry, you can stick with GUI.

The book is not perfect though. From time to time I missed explanation of the outcomes presented in the book -- the author does not have to spell out for me that I am looking at number "0.74", but the tip what does it mean is valuable (i.e. there are such explanations, but not in every desired place, at least by me). Second -- I would love to see more discussion about tough cases and how to handle them. Even such extremes, that surprised the author himself. Mistakes he did, the graphs that led him in the wrong direction, and so on. Such issues here are just lightly touched, not enough for my taste.

The biggest complain is not about the content itself, but the way Springer published this book. OK, it is in color, I love it, and I am grateful. But thanks to shiny paper and weird binding, not only I couldn't put this book flat but with every page turn I had to maneuver to kill the glare. In this regard, I can say it was unpleasant experience, because of arm fatigue (book is heavy!) and all the trouble.

Don't let those problems detract your from paying attention to this book -- if you are curious about data mining, or interested in it, and you are not an expert already -- solid buy!

R is becoming one of the leading tools of choice for serious data mining and quantitative analysis and is probably the most commonly used tool at the graduate school level today (it helps that it is open source and free!). But as a programming language, it has a steep learning curve, at least steeper than many of the more traditional GUI-based tools for quantitative and statistical analysis like SPSS or even SAS. This book provides a great introduction to both the topic of data mining and using the Rattle interface, which is a GUI built around typical data mining functions for the R language. If you work through this book in detail, you will learn a fair bit the basics of the R language as well as how to complete some basic data mining tasks inside the Rattle interface. For anyone looking to learn more about R, this would be a great introduction.

Very helpful, detailed, and simple approach to master data mining with Rattle. The book was written by Sr. Graham Williams, who actually developed Rattle, so you'll learn directly from the person who made Rattle.

As Rattle provides a quick hands-on to learn data mining and predictive modeling, the book itself will guide you through by introducing you to the Rattle's GUI functionality, and take you step by step from basic data analysis to advanced modeling algorithms such as SVM and Random Forest.

If you have no previous experience with R programming, and want to get started to learn R and predictive analytics, I highly recommend this book to you and introduce you to Rattle and give a head start with R.

Had to get this for a class but learned a lot from the materials in this book

It's been a while since I was this excited to read a book about my career, but Rattle is such amazing invention and this book does a great job with practical examples and beautiful color plots. I can't wait to start practicing some of the things I have learned from this book. R can be very tricky to learn, but this book makes it seem doable, and fun!

There's not much detail in how the algorithms are working, or much else. It's very basic 'click here.. now click here'. I was pretty disappointed in this book, although it does have some strengths in the over all presentation of the material, but it won't make you much more than a beginner in the subject.

Very helpful start to R. The Rattle interface is easy, intuitive and the ability to cut and past from its log to the R console is great.

No one uses the Rattle interface anymore. Everything else is covered in other books

I like this book very much, and have had even a better time exploring the associated algorithms. My only complaint is that the theory side is pretty skimpy to say the least - so do not expect to learn data-mining at a deep level from this book - but rather get a hands-on feel for what can be done - if a pretty vague notion at a conceptual level. Maybe the authors could try to improve this deficiency in later editions. Nonetheless, the book is great.

Me gusta mucho R y la herramienta Rattle. El libro la describe muy bien, muy buenos ejemplos. La editorial es excelente.

Rubbish book.

Good book for visualisation and data exploration. I felt that the data mining chapters went into too much depth on the actual algorithms and not into enough depth on the process and the parameter values. For someone new to R, this is a great first book, as it allows you to start using R without having to learn the programming language.