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15 of 17 people found the following review helpful
5.0 out of 5 stars Wholesome - no frills - data mining cooking !!, 9 Aug 2001
By 
Oliver Femminella (London, United Kingdom) - See all my reviews
This review is from: Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management (Datawarehousing) (Paperback)
By reading this book you should learn how to cook data mining applications...but if you have very little or no appreciation of data mining and customer relationship management (CRM), and you have never used SAS software, you'll probably end up burning your first few cakes or even worst your fingers !! As the author gives a very brief introduction to data mining, make sure before you even start reading this book that you have a grasp of statistical modelling and data mining in a CRM context, otherwise you will find the material presented in this book too much to take in at once, and worst, you may probably end up being put off building your own data mining applications. The author clearly has a solid statistical (read SAS) background, making this book a strong contender as one of the best books on data mining around, providing the reader with a number of useful recipes, practical examples and pragmatic data mining approaches which should be studied and understood in detail. Being a cookbook, the author's (or should I say the chef's) particular style may not suite your palate. In other words, you may not like the author's bias towards using logistic regression as the main data mining technique. As a result, you will not learn how to cook exotic dishes using ingredients such as neural networks. However, the choice to use logistic regression as the main statistical techniques pays off, as this allows the reader to start learning to cook robust/reliable meals (models), before cooking with the more exotic ingredients (techniques). The topics and interventions provided by the well-experienced contributors are in context with the author's material, strengthening the practical context in which data mining applications are presented. On a few occasions, I found that the author does not discuss figures and tabulated outputs in a straightforward way, inevitably affecting the readability of the book. Notwithstanding, the methodology and material presented has a considerable amount of depth and rigour, and the general themes are well structured and maintained throughout. Many figures and tabulated results are presented in the graphical output provided by the SAS system, which may be less appealing to you if you are not going to be using SAS. Also, many data mining software tools now available have significantly better graphical data presentation capabilities than those presented in this book, inevitably giving it a slightly dated look. Unsurprisingly, being the first version of the cookbook, there are a few typos (and one incorrect figure at the beginning of the first chapter). In summary, this book is not for the novice, but will be a book that you will want read more than once.
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