The new Nisbet, Elder, and Miner book is out now, and has been receiving good reviews on Amazon. A sampling of the 6 reviews so far (all 5 stars):
The “Handbook of Statistical Analysis & Data Mining Applications” is the finest book I have seen on the subject. It is not only a beautifully crafted book, with numerous color graphs, chart, tables, and screen shots, but the statistical discussion is both clear and comprehensive.
This is an extraordinary book. So often within this field books are offered as bibles only to fall short. This book does not and delivers a wide array of information and useful tips for the beginner and veteran data miner.
What I like about this book is that it embeds those methods in a broader context, that of the philosophy and structure of data mining writ large, especially as the methods are used in the corporate world. To me, it was really helpful in thinking like a data miner, especially as it involves the mix of science and art.
This is one of the few, of many, data mining books that delivers what it promises.
It has a great mix of data mining principles with step-by-step solutions (case studies) using data mining software, such as Clementine, Enterprise Miner and Statistica. It is this practical approach to data mining that fills a void in the current selection of books in the marketplace (and there are many great data mining books out there).
For some, the benefit of the book will be the case studies on Fraud Detection or Text MIning. For others, seeing how to solve problems using Enterprise Miner (or Clementine or Statistica) will be of most benefit, operating almost like a users manual. I most appreciated the first chapter on the history of statistics (Nisbet), Model Complexity and Ensembles (Elder) and the 10 Data Mining Mistakes (Elder).
One more quote, this from the second forward in the book:
This volume is not a theoretical treatment of the subject — the authors themselves recommend other books for this — but rather contains a description of data mining principles and techniques in a series of “knowledge-transfer” sessions, where examples from real data mining projects illustrate the main ideas. This aspect of the book makes it most valuable for practitioners, whether novice or more experienced.
The Handbook of Statistical Analysis and Data Mining Applications is an exceptional book that should be on every data miner’s bookshelf, or better yet, found lying open next to the computer.
— Dean Abbott, Abbott Analytics
