Customer Reviews

Data Mining Using SAS Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

5 star:		(2)
4 star:		(3)
3 star:		(0)
2 star:		(1)
1 star:		(0)

 

 

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.

Very cool time saving SAS macros are included in this book. I used these macros daily to generate SAS graphs, analysis, html outputs etc in SAS Learning edition. The author updates the macros frequantly and I found out about the SAS learning edition recently by visiting the books web page ...

It is a unique approch to use SAS.

SAS is not the best system for data mining to begin with. R (or S-plus) seems to provide a much stronger framework and packages for Classification and Regression Trees, Random Forests, Neural Networks etc. SAS cannot really do any of these with the base system.
All of the techniques covered in this book are fairly standard statistical techniques that don't really match with what I consider data mining to be (i.e. regression, principal component analysis, discrimination analysis). There are better texts for these techniques that cover both the theory and code more indepth.

It's a really interesting guide. It's friendly and complete.
Not so usefull in working with SAS Enterprise Miner, however it can help you to understand steps made with SAS Enterprise Miner.

Paolo

This is not a book for SAS programmers. This is a great book for a grad student taking a multivariate stats class with access to SAS and SAS macros. Dr. Fernandez writes in an easy going style, similar to his classroom lectures. He explains key concepts and terminology in a thorough manner. For students struggling with their own datasets that never seem to match the neatly organized datsets and graphs of the books' datasets, he offers some helpful explanations of topics like moderately large multivariate skewness and kurtosis. He also includes references and suggested readings at the end of each chapter for those of use who need a little more help. One deficiency of thebook is a lack of real world examples and interpretation of output.

This book is an excellent reference for people who are working for theses, dissertations, papers, etc. It provides readers with concepts, implementation methods and interpretations of results for a number of categories in data analyses. The contents in the book are accurate. Therefore the book can be trusted.

This book also gives SAS macros to readers. It makes readers work much more efficient.