- Paperback: 672 pages
- Publisher: John Wiley & Sons; 2nd Edition edition (8 April 2004)
- Language English
- ISBN-10: 0471470643
- ISBN-13: 978-0471470649
- Product Dimensions: 23.5 x 18.9 x 3.6 cm
- Average Customer Review: 4.2 out of 5 stars See all reviews (11 customer reviews)
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- #21 in Books > Business, Finance & Law > Sales & Marketing > Database Marketing
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Product Description
Product Description
- Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems
- Each chapter covers a new data mining technique, and then shows readers 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
- More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining
- Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis
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.
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First Sentence
Somerville, Massachusetts, home to one of the authors of this book, is also home to a woman from Cameroon who braids hair. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
Somerville, Massachusetts, home to one of the authors of this book, is also home to a woman from Cameroon who braids hair. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Customer Reviews
Most Helpful Customer Reviews
4 of 4 people found the following review helpful
Format:Paperback
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'.
(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'.
3 of 3 people found the following review helpful
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.
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.
3 of 3 people found the following review helpful
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.
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.
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Most Recent Customer Reviews
Fantastic conditions
The book arrived on time and in good conditions, as it was said. I think this book is a good introduction to the Data Mining world, as it explains basic techniques as well as the... Read more
Published 19 months ago by miquel
Clear, precise, well written, the best introduction to data mining I...
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... Read more
Published on 13 April 2009 by Yassine El Ouarzazi
Excellent book taking Statistical headache out of Datamining
As a DM Consultant, this book is a must read for anybody new to Data Mining, who wants to fully understand the techniques but doesn't want to get bogged down in specific... Read more
Published on 6 Dec 2001
Excellent discussion of a wide variety of techniques
Provides an excellent discussion of a wide variety of techniques and their applicability to business problems.
Published on 10 July 2001
Not mathematical enough.
Very disappointing, if you're looking for a mathematically oriented book. In fact it avoids math like the plague. Read more
Published on 25 Feb 1999
Excellent business-oriented introduction
Data Mining Techniques, For Marketing, Sales, and Customer Support strikes a good balance: business parts never get technical and technical parts are always to the point. Read more
Published on 19 Jun 1998
Technically accurate and enjoyable to read
The authors discuss data mining for marketing in a business context. Their descriptions of the techniques are clear and accurate, and the case studies provide excellent models. Read more
Published on 22 Jan 1998
Good Introduction book, not limited to Marketing
The authors explain in a detailed way the most popular Data Mining techniques. The topics about Neuronal Networks, Decision Trees, Market-Basket Analysis and Memory-Based Reasoning... Read more
Published on 9 Dec 1997
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