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.
It is a unique approch to use SAS.
This book also gives SAS macros to readers. It makes readers work much more efficient.