Handbook of Statistical Analysis and Data Mining Applications Hardcover – 21 May 2009

Far too long and repetitive.

brilliant material

It seems rare to find an actual book that lives up to it's description given by the authors as to the content and what can be expected when it comes to datamining topics. Generally the academic reviews are biased and favorable giving the practitioner a book that will be filled with proofs and formulas with little else to begin the implementation process.This book comes as advertised, a handbook with practical explanations of techniques coupled with references to software that will give a "good enough" solution to a finance problem.Others may complain that the book in fact is software specific but the topic area will require the use of sophisticated software and a supporting team to be of any value in the enterprise setting.For the lone wolf the book still provides enough of an overview of the topic areas to allow the reader to be familiar with the buzzwords and concepts used to begin the process.The authors have also kept the chapters short enough providing enough clarity in a few words without the clutter of academic textbook approaches that come with detailed bibliographies and references to obscure works that the practitioner has either no interest or desire to read as justification for the point being made.As a reference book, this is the one that should be on the desk of the practitioner who can refer to it when the young gun who stumbles into the office speaking in incomprehensible terms that can easily confuse the manager and lead to lost time and effectiveness in presenting the findings given by the intern hired for the summer.Nice to know that there are still writers with impeccable academic backgrounds writing books giving applied solutions for managers within a framework that uses generally available software packages as the learning vehicle.Must have reference for the analytics and datamining field's that will play much greater roles in finance and business decision making.

discussion proceeds in logical way- start by working one example then go back and get your questions answered. trail software download no problems. highly recommend but must buy hardcopy to get dvd. dvd not included in e download. Amazon very quick to help me cancel e download and order hardcopy once i became aware the content of the dvd not included in e download. complements to author and thanks to Amazon for working problem. With e download text easy read on pc screen not certain would be good read on K.

Clear examples, grounded on the evolution of the topic.This is an exhaustive book, which will take a couple of weeks to penetrate.

This is one of the few, of many, data mining books that delivers what it promises. It promises many detailed examples and cases. The companion DVD has detailed cases and also has a real 90 day trial copy of Statistica. I have taught data mining for over 10 years and I know it is very difficult to find comprehensive cases that can be used for classroom examples and for students to actually mine data. The price of the book is also very reasonable expecially when you compare the quantity and quality of the material to the typical intro stat book that usually costs twice as much as this data mining book.The book also addresses new areas of data mining that are under development. Anyone that really wants to understand what data mining is about will find this book infinetly useful.

Nisbet's, Elder's and Miner's book on statistical analysis, was effective in that it:- gave a intellectual insight into the thinking world of the analyst, as well as- defined a strong analytics process to follow and- gave you hands on examples and tool discussions that attempt to implement the intellectual concepts and process.In the cases of KXEN and SPSS's Clementine. I was able to download their trial software and use this book to exercise the concepts.I found KXEN's ability to automate the process (and leverage a database engine!) and do much of the analytics at the "click of a button" very powerful, reinforcing much of the book.It is clear that the "thinking" part of analytics is still important and critical to successful analytics, the automation in KXEN is an effective adjunct and accelerator to the expression of the analyst's intellectual thinking.

This book helps its readers to understand the Predict Analytics. I strongly recommend this book for anyone who wants to learn PA.

research

I like this book as a resource to use during data mining projects. It is a good for review and exploration of methods to augment the methods that I am most familiar with. I especially appreciate the practical application examples contained in the book. They provide enough detail to get a feel for whether the methods might be applied to other projects we are working on.

I wanted a DM book that systematically organized and explained the following:1. An overview of the most popular DM techniques2. A brief technical background for each technique3. A discussion on the best suited applications for each4. Some common usage guidelines (advantages/disadvantages/common mistakes etc)5. A few hands-on demos using commonly used tools.You may be thinking that this was too much to ask from one book. However, reading the top reviews, set expectations really high: this book had to be a home run! After spending time with it, i am disappointed to say the hit-ratio was only 20% for my needs (only #5).Most hands-on demos presented in the book are for the package, Statistica. But the authors seem to be conflicted between writing an user manual for Statistica and a book on business applications for DM. In the end neither objective is fully met.The best chapter is the one of Top 10 DM mistakes. This is brilliantly written and edited. As someone who has used modeling, simulation and analytical techniques all my professional career, i totally resonated with this chapter and it alone deserves the 2 stars given.

This is by far the most comprehensive and well written book on data mining. It is exceptionally well organized, and provides easy-to-understand descriptions of very complex processes and algorithms. Through tutorials and case studies, anyone from those just learning about data mining to experts will get great value from this book. The tutorials show step-by-step examples of how the leading tools on the market can be applied to solve an array of problems. Having developed and applied data mining tools for over 20 years, I have never seen a book that I would recommend more highly on this subject.

It is very well written and easy to follow. It does not go into the mathematics of statistical analysis, but it covers the concepts very well.

I had experience with many of the statistical tools that fall under the heading of data mining. There are good books on GAMs and so on. What I like about this book is that it embeds those methods in a broader context, that of the philosophy and structure of data mining writ large, especially as the methods are used in the corporate world. To me, it was really helpful in thinking like a data miner, especially as it involves the mix of science and art.I also had no experience with Statistica Data Miner but have been very impressed with the program relative to those that are less well documented (WEKA) and too darned expensive (SAS EM)The richness of the examples is so helpful.

Good read, touches upon broader aspects of data mining without delving too much into the mathematics of it. Good for any one interested in Data mining

This book is for practitioners, not for those seeking a deeper understanding of data mining. It both makes and delivers on that claim. All major data mining topics are covered, though in a necessarily shallow manner in keeping with the book's goal of getting past the theory and moving to the practice.Oddly, the very start of the book does have a bit of theory in the form of the historical roots of statistics and the limitations of statistics that leads to the need for data mining; I found this bit of history quite fascinating and enlightening; it is something I've found in few other data mining books, and I've read several.The trouble is, I do like theory a bit. I have a master's in computer science, so I'm a bit biased that way, thus my relatively low scoring of the work.About 1/3rd of the book is dedicated to working through real problems, and that is the overwhelming strength of the book. If you are one who learns by doing rather than by theorizing, you'll find this book outstanding.The biggest criticism I have of the book is that it is clear that there are significant parts where the authors just didn't have their hearts in it; it felt like they wrote certain sections because the publisher told them they had to in order to hit some type of target marketing segment.It's also unfortunate that all three software products provided expire in 90 days or less. I'm never one to accomplish anything in 90 days, let alone get through a 700-page technical work!!! I know they are the 3 top mining tools, but I much prefer RapidMiner, a product that is amazingly feature-rich, so easy to use it is actually fun, supported by a robust open-source model, and free.Overall, a solid work. But to me, theory matters, that's one star down; and rigorous, enthusiastic writing matters, so that's two stars down. In the 3-stars that remain is lots of hands-on practice if you don't mind expiring software, and for that it is very strong.

not really worth the price. half of the book is about a tool (Statistica) that it makes the book sound like a Sales-y book. the first part of the book is just fine; I enjoyed more "Data science for business" as an introduction book. "Statistical analysis and data mining" does not fit as an introduction book nor as an advanced book.