Features
- Cover Type: Hard Cover with 298 pages
- Published by: Wiley-Interscience December 11, 2006
- Written in: English
- ISBN 10 Number: 0470084855
- ISBN 13 Number: 978-0470084854
-
Book Dimensions:
10 x 7 x 0.8 inches
- Weighs: 14.4 ounces
Product Review
"Shmueli et al. have done a wonderful job in presenting the field of data mining…a welcome addition to the literature." (
Computing Reviews.com, August 15, 2007)
"This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices." (
IT Professional, January/February 2007)
"The book contains real case studies, providing yet further demonstrations of the extraordinary data wealth of the modern commercial world." (
International Statistical Review, 2007)
"…full of vivid and thought-provoking anecdotes…needs to be read by anyone with a serious interest in research and marketing." (
Research Magazine, August 2007)
Book Description
Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence
In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. Data Mining for Business Intelligence, which was developed from a course taught at the Massachusetts Institute of Technology's Sloan School of Management, and the
University of Maryland's Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models.
Featuring XLMiner, the
Microsoft Office Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of data mining techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding.
Data Mining for Business Intelligence:
* Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis
* Features a business decision-making context for these key methods
* Illustrates the application and interpretation of these methods using real business cases and data
This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an great learning tool for creating valuable strategies and making wiser business decisions.
Reader Reviews
Data mining is the extraction of useful information from large amounts of data. Perhaps the best example of this is Amazon. If you go to Amazon to look at a book, you'll find such tidbits of information as a section on the page headlined 'Customers who bought this item also bought' and another 'What do customers ultimately buy after viewing this item?' That's datamining, dozens or hundreds, or thousands of people looked at the page about this item. Then they went on to take these other actions. Among all the data that Amazon has collected they mine their database and pull out information to fill in these blocks. This book, intended for MBA level students gives an excellent introduction to data mining. It further includes access to an Excel add-in called XLMiner that is specifically set up to allow the student to use Excel to learn how data mining is done. The one thing I would ask the authors to do in their next edition is to provide a brief review of the commercially available data mining software products that are available. If not all of the software, perhaps just the top half dozen or so. In real life we aren't going to use Excel for data mining, our data resides in a database somewhere.
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