Download Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, by Michael J. A. Berry

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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, by Michael J. A. Berry

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From the Author

Professors using this book as a text and readers learning on their own can benefit from exercises and data sets provided by JMP at jmp.com/en_us/academic/data-mining-techniques.html.  The exercises were prepared by one of the authors (Michael Berry).

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From the Back Cover

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.

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Product details

Paperback: 888 pages

Publisher: Wiley; 3 edition (April 12, 2011)

Language: English

ISBN-10: 0470650931

ISBN-13: 978-0470650936

Product Dimensions:

7.4 x 1.9 x 9.2 inches

Shipping Weight: 2.7 pounds (View shipping rates and policies)

Average Customer Review:

4.0 out of 5 stars

31 customer reviews

Amazon Best Sellers Rank:

#432,883 in Books (See Top 100 in Books)

I got this book for a class on Data-Mining and I found it to be a very good book. It has good visuals to help the reader understand the concepts in the book and maintains a good sense of humor throughout so reading it doesn't seem as dense as some of my typical statistics books. My only criticism of the book would be that it never discusses common software platforms for performing these tasks. While I understand that he probably didn't want to favor a particular platform over another, it seems that introducing the major ones could be helpful for people that may be very used to using just one.

In a field evolving as dynamically as data science, 2011 seems a long time ago, and I've since bought a number of the newer titles out there. Still, however, I often find myself reverting to Linoff and Barry's text for a lucid explanation of, or interesting take on a particular data mining subject area.The book is thorough (at 800+ pages this should be the expectation) and technical, but isn't really a how-to manual in that it stops short of containing actual code or instructions. That's not an issue, however, as such instructional information is available elsewhere if needed.My only complaint about the work is that it is a little redundant and otherwise verbose at times. I hope a fourth edition is forthcoming, and that it is a little more tightly edited.---Z. KhandwalaInstitute for Advanced AnalyticsBellarmine University - Louisville, KY

I taught myself data mining using this book. I also was in my MBA Decision Science class the next year and they used the exact same book, but I had already read the book front to back. I aced that class. The book is comprehensive and allows people to grasp all the basic concepts of data mining.

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.

Similar to Excel 15 years ago, Data Mining Techniques are the new required skill set for business professionals. Learning techniques from a professionals Gordon Linoff and Michael Berry provides an excellent foundation

Very detailed and covers a lot of topics.

The book provides a very general overview of data mining concepts. It is primarily for non-technical audience. Its chapters on statistical methods are weak. The best chapters are on neural nets and clustering. Text mining is incredibly superficial.

its ok, I use it for course material. Not great but it will work for the price. Thanks you much

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