Average Reviews:
(More customer reviews)Overall, this book is quite good. I wish I had it when I was working on my dissertation. I used all of the techniques in this book with SAS, and had to figure out this stuff on my own. I wish I hadn't had to go through all that, although it was a good learning experience. If I had had a resource like this book then I could have devoted more time to exploratory analysis and less time to the nuts and bolts of getting the programs to run.
I only gave it 4 stars however, because I thought the last chapter should be removed entirely. Reading Chapter 7 made me doubt the author throughout the whole rest of the book, which is not a good sign. Problems, some minor: neural networks are hardly an "emerging technology" for data mining. "Data warehousing" is not really a data mining technology, at least not how "technology" is defined throughout the whole rest of the book (i.e. as a technique). First and foremost, data warehousing is an organizational method for data, not a technique for analyzing it (which is what every single other topic covered in this book is designed to do: analyze). Market basket analysis is just ONE WAY to describe association rule mining. All association rules are NOT designed to help with marketing. This author obviously read Barry & Linoff (Data Mining for Marketing, Sales, and Customer Support) for his definition of what association rules are good for, and missed the whole point of how they can be used with ANY data, not just marketing data. Lastly, his bibliography in Chapter 7 is very thin; if his treatment of these "emerging technologies" is going to be so cursory, at least give the reader some decent pointers to more appropriate texts.
Another picky point: the beginning of the book talks about how it is designed to show how to use SAS if you DON'T have Enterprise Miner (which is good, since that's a really expensive thing that most students and faculty can't afford). And then this last chapter begins by saying how it will cover three emerging technologies for which you can use SAS ... which is all well and good, until you get to the last sentence of the last chapter, which says that you must use Enterprise Miner to do anything with these three emerging technologies. How frustrating for the reader who thinks they're going to cover emerging technologies the same way as the whole rest of the book!
Anyway, just ignore the last chapter of this book and enjoy the first 6. This book will definitely save you some time if you are interested in prediction, classification, clustering, principal components analysis, or common factor analysis.
Click Here to see more reviews about: Data Mining Using SAS Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Most books on data mining focus on principles and furnish few instructions on how to carry out a data mining project. Data Mining Using SAS Applications not only introduces the key concepts but also enables readers to understand and successfully apply data mining methods using powerful yet user-friendly SAS macro-call files. These methods stress the use of visualization to thoroughly study the structure of data and check the validity of statistical models fitted to data.
Learn how to convert PC databases to SAS data
Discover sampling techniques to create training and validation samples
Understand frequency data analysis for categorical data
Explore supervised and unsupervised learning
Master exploratory graphical techniques
Acquire model validation techniques in regression and classificationThe text furnishes 13 easy-to-use SAS data mining macros designed to work with the standard SAS modules. No additional modules or previous experience in SAS programming is required. The author shows how to perform complete predictive modeling, including data exploration, model fitting, assumption checks, validation, and scoring new data, on SAS datasets in less than ten minutes!
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