The book from Robert Nisbet, John Elder and Gary Miner is a fresh addition to any data miner library. First surprise: the book is in full colors with a lot of pictures which is a good point. With a focus on data mining applications, the book also covers introduction and more profound data mining concepts. The book isn’t very technical and doesn’t contain equations and complex formulas. However, complex data mining challenges are addressed. The main advantage of the book is to give examples of each concept using three famous data mining tools: SPSS, SAS and STATISTICA.

The biggest added value of the book, and what makes it a unique resource in the field, are the tutorials. Several applications are detailed: aviation safety, unsatisfied customers, credit scoring and process control among others. Whatever your field of application is, you will find useful examples in the book. Even if the case studies aren’t in your field of interest, reading them takes you to an amazing journey in the world of analytics.

To be noted the interesting chapters about text mining and fraud detection. In the last part of the book, five chapters provide expert advices to both beginners and experienced data miners. The chapter “Top 10 Data Mining Mistakes” is remarkable. To conclude, this book is about applying data mining, rather than programming it. It gives excellent examples of enterprise applications in analytics.

Handbook of Statistical Analysis and Data Mining Applications

I have also read part of this book. I found that it is a very good book for practitioners but lack a little bit theory for people interested by theory. But it is well written and the color illustrations are great.

Phil: You are right, and this book is not intended to provide a theoretical foundation for data mining; there are other books that do that quite well. The key to this book is the practitioner-level “rules of thumb” that the book provides.

(to be fully transparent here, I wrote one of the forwards to the book)