Handbook of Statistical Analysis and Data Mining Applications (Hardcover)

~ (Author), (Author), Gary Miner (Author)

Editorial Reviews

Review

Data mining practitioners, here is your bible, the complete "driver's manual" for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here.

Going beyond its responsibility as a reference book, this resource also provides detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success.

If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner.

- Eric Siegel, Ph.D., President, Prediction Impact, Inc. and Founding Chair, Predictive Analytics World

"Great introduction to the real-world process of data mining. The overviews, practical advise, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners."

--Karl Rexer, PhD (President & Founder of Rexer Analytics, Boston, Massachusetts)

Review

Data mining practitioners, here is your bible, the complete "driver's manual" for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here.

Going beyond its responsibility as a reference book, this resource also provides detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success.

If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner.

- Eric Siegel, Ph.D., President, Prediction Impact, Inc. and Founding Chair, Predictive Analytics World

"Great introduction to the real-world process of data mining. The overviews, practical advise, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners."

-- Karl Rexer, PhD (President & Founder of Rexer Analytics, Boston, Massachusetts)

See all Editorial Reviews

Product Details

• Hardcover: 864 pages
• Publisher: Academic Press; Har/DVD edition (June 5, 2009)
• Language: English
• ISBN-10: 0123747651
• ISBN-13: 978-0123747655
• Product Dimensions: 9.3 x 7.8 x 1.5 inches
• Shipping Weight: 3.4 pounds (View shipping rates and policies)
• Average Customer Review: 4.6 out of 5 stars  See all reviews (9 customer reviews)
• Amazon.com Sales Rank: #42,824 in Books (See Bestsellers in Books)

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  #14 in	Books > Computers & Internet > Software > Mathematical & Statistical
  #15 in	Books > Computers & Internet > Databases > Data Mining
  #70 in	Books > Professional & Technical > Professional Science > Mathematics > Applied > Statistics

Customer Reviews

Most Helpful Customer Reviews

13 of 13 people found the following review helpful:

5.0 out of 5 stars The top data mining text on the market, June 18, 2009

By	Joseph Hilbe (Florence, AZ United States) - See all my reviews (REAL NAME)

The "Handbook of Statistical Analysis & Data Mining Applications" is the finest book I have seen on the subject. It is not only a beautifully crafted book, with numerous color graphs, chart, tables, and screen shots, but the statistical discussion is both clear and comprehensive.

The text does not use only one statistical data mining application to display examples, but provides a rather thorough training in the use of both SAS-Enterprise Miner and STATISTICA Data Miner. A section on SPSS Clementine is also provided, giving comparisons between the various packages. Also employed are STATISTICA's C&RT, CHAID, MARSpline, and other data mining and graphical analytic tools.

The text does not burden the typical data mining researcher with the internals of how the various tools work. It is therefore not steeped in equations. Some are to be found, of course, but the emphasis is on understanding the concepts involved and on how to apply these concepts to real data - which is provided to the reader in terms of data tutorials. Specialized datasets have been prepared by both authors and outside experts in various areas of inquiry ranging from entertainment, financial, engineering, clinical psychology, dentistry, demographics, medical informatics, meteorology, astronomy, and more. Each tutorial is associated with data stored on either the associated CD that comes with the book, or which can be downloaded from a companion web site. Worked out examples of how to use data mining techniques on such data is provided to help the reader gain a solid feel for the data mining enterprise. The final third of the book is devoted to a partial selection of the available tutorials. The two earlier chapters demonstrate how to use data mining software for the analysis of data.

I highly recommend this work to anyone having an interest in data mining. I might also add that the Amazon price of $72.37 is truly excellent for an 864 page academic text, having full color tables and screen shots on some one-third of the pages, plus a CD. A bargain indeed.

12 of 12 people found the following review helpful:

5.0 out of 5 stars Excellent Source, June 16, 2009

By	Joseph Somma "jsomma" (Buffalo, NY) - See all my reviews (REAL NAME)

This is an extraordinary book. So often within this field books are offered as bibles only to fall short. This book does not and delivers a wide array of information and useful tips for the beginner and veteran data miner.

Prior efforts to write a data mining bible have been limited to particular software. Statistical Analysis and Data Mining provides examples from a multitude of data mining software. The examples provided are concise and easy to follow.

Chapter 20, Top 10 Data Mining Mistakes, was one of my favorites. It provided a concise discussion of pitfalls all miners should be aware of and guard against.

Finally, the tutorials (Section III), provide a substantial review of the mining process. The 13 examples cover a wide range of problems and will imbue the reader with a good sense of how data mining works in different scenarios.

Statistical Analysis and Data Mining should be an essential component of every data miner's library.

7 of 7 people found the following review helpful:

5.0 out of 5 stars I really liked this book, June 27, 2009

By	Anonymous "Anonymous" (Nunya, USA (business address)) - See all my reviews

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.