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am 9. Dezember 1997
The authors explain in a detailed way the most popular Data Mining techniques. The topics about Neuronal Networks, Decision Trees, Market-Basket Analysis and Memory-Based Reasoning are excellent. I think the topic Genetic Algorithms could be a bit more developed, but for the beginner is a good first overview. I have missed a topic about fuzzy logic. Given that the 90% of Data Mining projects are based on Marketing (1:1), the book is absolutely suitable for starting with these concepts, although I feel the book can be used in any other field (Just-In-Time Inventory, Demand Forecasting, Supply Value Chain, etc.)

In my opinion, it was very useful for my work and I considered it as a reference book.
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am 4. Mai 1999
It covers almost every aspect of data mining. It is clear, precise, goes to the point, and sometimes goes into some depth. If you are a marketing person, it will give you a very refined idea of what can be done with data mining, and what does it involves for your company ( remember the first thing you need is DATA!!). If you are an academic person, you will get a general idea of the different kinds of data analysis. You won't see any formulae or algorithms, after reading this book look for details somewhere else.
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am 1. April 2000
This book is chock full of the syntactic ambiguities which make relatively simple concepts, such as MBR, difficult to explore.
e.g. "That is, 80 percent of the codes assigned by MBR were correct, but the cost was that 28 percent of the codes assigned were incorrect."
Luckily, there's a picture explaining the mess.
The ideas are here, but Berry and Linoff need to hire a real writer. I guess, smart folks like these guys have a tough time communicating with the common man?
- Merv
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am 25. Februar 1999
Very disappointing, if you're looking for a mathematically oriented book. In fact it avoids math like the plague. It's therefore ideally suited to: (a) project managers who don't really want to do any serious work themselves, and (b) people who want to drop words at cocktail parties to impress people. Totally unsuited to the serious researcher.
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am 5. November 1999
This book labors to explain that Data Mining is possible. It spends 3 chapters explaining the business process flow of data mining as: 1. Figure out your business problem 2. data mine to figure out what to do 3. do it 4. measure to see if you did anything.
It's nicely written, but VERY BASIC
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am 19. Juni 1998
Data Mining Techniques, For Marketing, Sales, and Customer Support strikes a good balance: business parts never get technical and technical parts are always to the point. This book is rich with examples from real business cases. I consider this the best introduction in data mining currently available.
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am 31. März 1999
Forget the negative review of the guy from Tempe: if you're not interested in the math, you can make do with the descriptions and if you know enough math the descriptions are enough for you to reconstruct the algorithms. This is a fine introductory book to the subject.
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The authors discuss data mining for marketing in a business context. Their descriptions of the techniques are clear and accurate, and the case studies provide excellent models. The book is very well written and has a comprehensive index.
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am 28. August 1999
When I started data mining (over 25 years ago)after graduating from MIT, neither the name nor the techniques existed. I had to develop both concepts and approaches that were then applied to situations as varied as evaulating the US SBA for the Federal Government to mapping the market for and product requirements of ATM machines for a Fortune 500 to developing a strategy to export shoes (leading to a tripling of industry exports) for the Commerce Dept. to predicting the lodging demand in Orlando, FL during the gas crisis for a series of majorinvestors to making sense of survey data for a major credit card company. The approach accurately described the situation in all of these cases and led to major management decisions.
The authors go beyond what I used in those days, describing a series of techniques and their applicability - through easily understood case studies.
This is not only an intelligent book, it is a very easy read. It doesn't go into mathemetical complexities because that is unnecessary. There are many treatises available on those subjects. Just surf the web to find some of them.
This book provides the information required by managers, leaders, and entrepreneurs to understand their marketplaces and customers.
Having run many companies and turned around a lot more, it is clear that this understanding is often sorely missing. Once gained, sharp increases in profitability usually result. The trick is something like an "aha" effect, an invention. The data is stored in a person's unconscious and, through predictable processes, pops out somewhat on demand.
Data mining is the process of causing similar "aha's" to pop out of the unconscious, the collective memory, of a company.
I recommend this book highly as a way to gain an effective understanding of how to do this.
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