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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (Englisch) Taschenbuch – 1. April 2011


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The newest edition of the leading introductory book on data mining, fully updated and revised
 
Who will remain a loyal customer and who won't? Which messages are most effective with which segments? How can customer value be maximized? This book supplies powerful tools for extracting the answers to these and other crucial business questions from the corporate databases where they lie buried. In the years since the first edition of this book, data mining has grown to become an indispensable tool of modern business. In this latest edition, Linoff and Berry have made extensive updates and revisions to every chapter and added several new ones. The book retains the focus of earlier editions-showing marketing analysts, business managers, and data mining specialists how to harness data mining methods and techniques to solve important business problems. While never sacrificing accuracy for the sake of simplicity, Linoff and Berry present even complex topics in clear, concise English with minimal use of technical jargon or mathematical formulas. Technical topics are illustrated with case studies and practical real-world examples drawn from the authors' experiences, and every chapter contains valuable tips for practitioners. Among the techniques newly covered, or covered in greater depth, are linear and logistic regression models, incremental response (uplift) modeling, naïve Bayesian models, table lookup models, similarity models, radial basis function networks, expectation maximization (EM) clustering, and swarm intelligence. New chapters are devoted to data preparation, derived variables, principal components and other variable reduction techniques, and text mining.
 
After establishing the business context with an overview of data mining applications, and introducing aspects of data mining methodology common to all data mining projects, the book covers each important data mining technique in detail.
 
This third edition of Data Mining Techniques covers such topics as:
* How to create stable, long-lasting predictive models
* Data preparation and variable selection
* Modeling specific targets with directed techniques such as regression, decision trees, neural networks, and memory based reasoning
* Finding patterns with undirected techniques such as clustering, association rules, and link analysis
* Modeling business time-to-event problems such as time to next purchase and expected remaining lifetime
* Mining unstructured text
 
The companion website provides data that can be used to test out the various data mining techniques in the book.

Über den Autor und weitere Mitwirkende

GORDON S. LINOFF and MICHAEL J. A. BERRY are the founders of Data Miners, Inc., a consultancy specializing in data mining. They have jointly authored two of the leading data mining titles in the field, Data Mining Techniques and Mastering Data Mining (both from Wiley). They each have decades of experience applying data mining techniques to business problems in marketing and customer relationship management.

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Amazon.com: 15 Rezensionen
2 von 2 Kunden fanden die folgende Rezension hilfreich
Needs major restructuring 26. März 2014
Von cool_einstein - Veröffentlicht auf Amazon.com
Format: Taschenbuch
This book has useful nuggets but one needs to be patient to weed through ill-structured content.

Problem1: Examples and content repeats quite a bit across chapters, but unfortunately never discusses things properly at one place. In every edition authors have added chapters but seemed to have forgotten what they have already discussed in earlier chapters.

Problem2: Many suggestions, scenarios have been incompletely discussed. Without enough information one has to assume quite a bit about the scenario, problem, solution and the value of it. It is okay if it had happened once in a while, but this sprinkling of anecdotes without fully discussing is rampant in this book.

Problem3: It is quite verbose.

Problem4: Keeps on changing the depth of the discussion. The discussion is overall at high-level, however at times authors would go really deep to discuss details around some random topic eg calculation of silhouette scores. The primary focus seemed to be business people and not statistics students. Going deep "selectively" is also a big problem in this book.

This book has the potential to become a really good book, but it needs major restructuring.
1 von 1 Kunden fanden die folgende Rezension hilfreich
Excellent 15. Oktober 2013
Von Christopher Reilly - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
Very good introduction to newer data miners, but also comprehensive enough to use as perhaps the only guidebook you will need to understand, choose, and implement analysis techniques.
1 von 1 Kunden fanden die folgende Rezension hilfreich
Very verbose 15. Juni 2013
Von Dan Bowker - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
The author(s) has a way with words, he/they say in 10,000 words what could be said in 1000. I found myself skimming more than reading and eventually gave up. I think I made it through the first few chapters but that's all.
1 von 1 Kunden fanden die folgende Rezension hilfreich
Solid introduction to data mining 10. Juni 2013
Von M. Collins - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
I haven't made it through the entire book, but this serves as a solid reference for different topics in data mining. I used it in a graduate level course I took this spring and it was easy to read and understand.
1 von 1 Kunden fanden die folgende Rezension hilfreich
Really technical 12. Juli 2014
Von Laurel Fedor - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
This is not a book for a beginner. It really should be used for somebody who already uses a lot of data and is comfortable working with various programs. It's good to advance your knowledge but not begin it.
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