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Type of bind: Paperback
Dewey Decimal Number: 658.802
EAN num: 9780471470649
ISBN number: 0471470643
Label: *Wiley Computer Publishing
Manufacturer: *Wiley Computer Publishing
Quantity: 1
Page Count: 672
Printing Date: April 09, 2004
Publishing house: *Wiley Computer Publishing
Sale Popularity Level: 26357
Studio: *Wiley Computer Publishing
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Editor's Notes and Comments:
Product Description:
* Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems
* Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support
* The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining
* More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining
* Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis
User popularity level:
Rated by buyers 
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Be careful, the very first edition is MUCH older. Make sure you get the current 2004 edition.
There are most recent books, but this one is still worth reading first. This is especially true is you are an analyst. Managers of analysts might enjoy Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart or Competing on Analytics: The New Science of Winning, but analysts will need much more detail.
This the best single volume on Data Mining you can buy. As one who mostly teaches methodologies, I like that all the major topics are here: neural nets, market basket, cluster, and trees. But there are also techniques that SPSS and Clementine (the software packages I use) can not do like "link analysis". Also, unlike Larose Discovering Knowledge in Data: An Introduction to Data Mining, the data preparation reads like preparing data for data mining, not a carbon copy of preparing data for statistics. Regarding this issue see the excellent Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems). I have pretty much concluded that a data mining book that does not make clear that data mining and OLAP are not the same is not a great book. This book has an extended section on just that. It is highly readable and comprehensive.
Rated by buyers -
As a novice to data mining, I was searching for a book that would explain the concepts, NOT mathematical formulas. This text provides the reader with a clear and comprehensive explanation of each concept, provided examples, and the readability is excellent. Who should read the book? Anyone in business - marketers to CEOs; and college students at all levels who are trying to understand data mining concepts. The book is not for mathematicians who are searching for algorithms. I would rate this book 5-stars.
Rated by buyers 
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I'm very interesting in Data Mining and i think that this book is a good introduction to this field. Thanks Amazon
Rated by buyers -
Anyone interested in automating and improving decisions should have this book. It is one of the classic works on data mining and well worth the read.
I really liked the book both because it is well written and because, although it drilled into a fair amount of detail about some of the techniques, it started each new section off at a high level. This allows someone without a statistical background, such as me, to read as far as I can in each section and then skip ahead to the subsequent technique. This is a nice change from books that simply get more and more detailed as page follows page, preventing you from gaining an overview of the subject.
The book introduces data mining and a methodology for applying it, talks about some of the applications in "Marketing, Sales, and Customer Relationship Management" (as the subtitle puts it), walks through some statistical techniques and then spends the bulk of the book on various data mining techniques. It wraps up with a nice summary of how data mining plays with other technologies and with some practical advice on getting started.
One of the best summaries of where data mining fits is given early in the book where an enterprise is encouraged to:
- Notice what its customers are doing
- Remember what it and its customers have done over time
- Learn from what it has remembered
- Act on what if has learned to make customers more profitable
The authors point out that Data Mining is focused on the "Learn" stage or, as they put it data mining suggests but businesses decide.
The methodology section, and the subsequent notes that relate to applying these techniques in real life, talked about the feedback loops between steps in data mining - there is not a linear "waterfall" sequence of steps but constant iteration and learning. They also emphasized the importance of finding the right business problem at the beginning - start as someone once said, with the end in mind. This was reiterated when they quote Voltaire who said "Le mieux est l'ennemi du bien" ("The best is the enemy of good"). In other words, don't get hung up on trying to find the perfect algorithm, perfect answer. Instead build something that is good, that works, and learn and improve over time.
The authors made a big point out of the value of data mining for "mass intimacy", where you want to treat customers differently and there is a business reason to do so but where customers are too numerous to be assigned to staff. One of the issues they pointed out was that staff must be trained in customer interaction skills while also using all the data you have. The value of data mining in building a customer-centric organization cannot be overestimated.
Rated by buyers -
This well-written book is an excellent introduction to the data mining and predictive analytics space. The reader should be comfortable with data and data analysis. The reader, however, does not need any pre-existing knowledge specific to data mining and predictive analytics. Much of the book, including the middle chapters which describe specific analytic techniques, has general applicability to business problems beyond CRM.
I am an actuary working in the insurance industry and am ordering my second copy of the book.
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