Buy Used
£3.52
+ £2.80 UK delivery
Used: Very Good | Details
Condition: Used: Very Good
Comment: Ships from the UK. Former Library book. Great condition for a used book! Minimal wear. 100% Money Back Guarantee. Your purchase also supports literacy charities.
Trade in your item
Get a £1.44
Gift Card.
Have one to sell?
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See this image

Data Mining Techniques: for Marketing, Sales and Customer Relationship Management Paperback – 8 Apr 2004


See all 3 formats and editions Hide other formats and editions
Amazon Price New from Used from
Paperback
"Please retry"
£20.58 £3.52

There is a newer edition of this item:



Trade In this Item for up to £1.44
Trade in Data Mining Techniques: for Marketing, Sales and Customer Relationship Management for an Amazon Gift Card of up to £1.44, which you can then spend on millions of items across the site. Trade-in values may vary (terms apply). Learn more

Product details

  • Paperback: 672 pages
  • Publisher: John Wiley & Sons; 2nd Edition edition (8 April 2004)
  • Language: English
  • ISBN-10: 0471470643
  • ISBN-13: 978-0471470649
  • Product Dimensions: 18.8 x 3.5 x 23.6 cm
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Bestsellers Rank: 860,519 in Books (See Top 100 in Books)
  • See Complete Table of Contents

More About the Author

Discover books, learn about writers, and more.

Product Description

From the Back Cover

The unparalleled author team of Berry and Linoff are back with an invaluable revised edition to their groundbreaking text The world of data mining has changed tremendously since the publication of the first edition of Data Mining Techniques in 1997. For the most part, the underlying algorithms have remained the same, but the software in which the algorithms are imbedded, the databases to which they are applied, and the business problems they are used to solve have all grown and evolved. With that in mind, Michael Berry and Gordon Linoff–the leading authorities on the use of data mining techniques for business applications–have written a new edition to show you how to harness fundamental data mining methods and techniques to solve common types of business problems. Berry and Linoff’s years of hands–on data mining experience is reflected in every chapter of this extensively updated and revised edition. They discuss core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis. In addition, they provide an overview of data mining best practices. Each chapter covers a new data mining technique and then immediately explains how to apply the technique for improved marketing, sales, and customer support. The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining for both business professionals and students. With more than forty percent new and updated material, this second edition of Data Mining Techniques shows you how to: Create stable and accurate predictive models Prepare data for analysis Create the necessary infrastructure for data mining at your company The companion Web site provides exercises for each chapter, plus data that can be used to test out the various data mining techniques in the book.

About the Author

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

Customer Reviews

4.2 out of 5 stars
5 star
3
4 star
0
3 star
0
2 star
1
1 star
0
See all 4 customer reviews
Share your thoughts with other customers

Most Helpful Customer Reviews

7 of 7 people found the following review helpful By J. Porter on 6 July 2009
Format: Paperback Verified Purchase
This book has reasonable coverage of the basic algorithms used in simple data mining. However, it has the huge flaws that:

(a) Everything is introduced in a vague, hand-wavy kind of way, without any kind of precision. I know that mathematics is a turn-off for some people, but how many of those people are going to be trying to learn about data-mining algorithms? More precision is required. As a result of its lack complexities are hidden away..E.g. training neural networks is made to look simple, and there's a throw-away comment that back-propogation is not now used, another, undefined and unreferenced, algorithm being preferred.
(b) There's too much waffle about why you might want to mine data. That's fine, and there's reason for a book about precisely that to be written. But not the same book that explains what K-means clustering is. So there are two books here, a (possibly) good book about why a business might want to data mine, and the pitfalls etc it might experience, and a (very) bad book about the mechanics of data mining.

So don't buy it if you want to learn about data mining. For an elementary, but rigorous, introduction, may I recommend Bramer's 'Principles of Data Mining'.
1 Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
3 of 3 people found the following review helpful By James Taylor on 6 July 2007
Format: Paperback
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.
Read more ›
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
3 of 3 people found the following review helpful By E. Kraev on 13 May 2007
Format: Paperback
This book does what few others manage - namely, go through an immense amount of material using almost no math at all, so it's a pleasure to read, and discussing not just what the techniques are, but what they do, what they're good for, and what weaknesses each has.

On the other hand, the book gives enough detail on each method that it's completely clear how the math goes, and I could (and did) write the math easily for the methods I was interested in.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
Format: Paperback
No equations nor mathematical proofs, if you want those, steer away from this book. Otherwise, it is filled with clear intuitive explanations, with real-life business / social applications. I strongly recommend this book I have already bought it for several colleagues and professionals.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again

Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 8 reviews
7 of 7 people found the following review helpful
Data Mining book you should read first 28 Dec. 2008
By Keith McCormick - Published on Amazon.com
Format: Paperback Verified Purchase
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 of 13 people found the following review helpful
Everyone Should Do This 2 Feb. 2005
By John Matlock - Published on Amazon.com
Format: Paperback
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 of 7 people found the following review helpful
A must-have book for your technical library 28 Dec. 2006
By James Taylor - Published on Amazon.com
Format: Paperback
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 of 7 people found the following review helpful
Excellent introduction 7 Sept. 2005
By T. Sawhney - Published on Amazon.com
Format: Paperback Verified Purchase
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 of 16 people found the following review helpful
Practical examples not convincing, lack of benchmarking 17 Jun. 2005
By Vincent Granville - Published on Amazon.com
Format: Paperback
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.)
Were these reviews helpful? Let us know


Feedback