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Data Mining Techniques (2Nd Ed.) (Englisch) Taschenbuch


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Amazon.com: 8 Rezensionen
7 von 7 Kunden fanden die folgende Rezension hilfreich
Data Mining book you should read first 28. Dezember 2008
Von Keith McCormick - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
Be careful, the 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.
11 von 13 Kunden fanden die folgende Rezension hilfreich
Everyone Should Do This 2. Februar 2005
Von John Matlock - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Data mining is such a simple thing that you wonder why more companies don't do a better job of mining their own data sitting on their own hard disks.

If a customer buys the first in a series of mystery novels, who better to send a note telling him that the second book is now available. That's the essence of data mining. This would allow you to get a much higher return on your mailing, saving money and increasing return on your marketing.

This is one of those books that you need to read every few months. Each time you go through it you will find some idea that will enable you to get more out of your data. It isn't a book heavy on programming, but on the concepts that have worked for others.

Highly recommended.
6 von 7 Kunden fanden die folgende Rezension hilfreich
A must-have book for your technical library 28. Dezember 2006
Von James Taylor - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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 next 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.
6 von 7 Kunden fanden die folgende Rezension hilfreich
Excellent introduction 8. September 2005
Von T. Sawhney - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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.
12 von 16 Kunden fanden die folgende Rezension hilfreich
Practical examples not convincing, lack of benchmarking 17. Juni 2005
Von Vincent Granville - Veröffentlicht auf Amazon.com
Format: Taschenbuch
While the book is easy to read and not too technical, the applications investigated by the authors are too simplistic and not really convincing as to why we should use advanced techniques. It would have been nice to add an additional, more detailed chapter comparing the various implementations of data mining techniques by software companies (SAS Entreprise Miner, Clementine, Insightfull Miner, etc.)
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