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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management by [Linoff, Gordon S., Berry, Michael J. A.]
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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management Kindle Edition

4.0 out of 5 stars 2 customer reviews

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Length: 891 pages Enhanced Typesetting: Enabled Page Flip: Enabled
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Product description

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.

About the Author

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.


Product details

  • Format: Kindle Edition
  • File Size: 15247 KB
  • Print Length: 891 pages
  • Publisher: Wiley; 3 edition (23 Mar. 2011)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B004UB2KE4
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Screen Reader: Supported
  • Enhanced Typesetting: Enabled
  • Average Customer Review: 4.0 out of 5 stars 2 customer reviews
  • Amazon Bestsellers Rank: #446,624 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Top Customer Reviews

Format: Kindle Edition Verified Purchase
It is a good book for beginners!
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Format: Paperback Verified Purchase
Good overview. Cross-section by every important technique with mentioning other stuff like OLAP cubes. Will give You a notion about every important thing in data mining, but won't tell much about details of described techniques. But still worth reading!
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Most Helpful Customer Reviews on Amazon.com (beta) (May include reviews from Early Reviewer Rewards Program)

Amazon.com: 4.1 out of 5 stars 28 reviews
5 of 5 people found the following review helpful
4.0 out of 5 stars In a field evolving as dynamically as data science, ... 23 Feb. 2015
By Zain Khandwala - Published on Amazon.com
Format: Paperback Verified Purchase
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. Khandwala
Institute for Advanced Analytics
Bellarmine University - Louisville, KY
2.0 out of 5 stars Who is the audience? 3 May 2017
By Lucky Fin - Published on Amazon.com
Format: Kindle Edition Verified Purchase
Not a fan. I had to read the book for a class. Outside of that, I'm not sure who the audience is. It's painfully long winded with much academic level detail with minimal examples of practical application. I'd rather the minutia trimmed back and more examples of how the theories are used in the real world.
2 of 2 people found the following review helpful
5.0 out of 5 stars Good Book - Highly Recommended. 2 July 2014
By Geoffrey M. Lucas - Published on Amazon.com
Format: Paperback Verified Purchase
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.
1 of 1 people found the following review helpful
5.0 out of 5 stars I found a lot of useful information from examples in different industries 6 Aug. 2015
By J. Su - Published on Amazon.com
Format: Paperback Verified Purchase
I have read a couple of books about data science. Reading this one is most enjoyable. I cannot put it down. I found a lot of useful information from examples in different industries. Highly recommend. I do have years of hands on experience on data mining.
5.0 out of 5 stars I taught myself data mining using this book. I ... 9 Sept. 2015
By M - Published on Amazon.com
Format: Paperback Verified Purchase
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.
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