Product Description
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 excellent learning tool for creating valuable strategies and making wiser business decisions.

CUSTOMER REVIEWS (Average Customer Rating: 4.5 based on 7 reviews)

Practical hands-on introduction to data mining

This book is a great hands-on introduction to data mining. It comes bundled with XLMiner, an Excel add-in that implements popular classification and prediction methods. The chapter examples, problems, and datasets are interesting and challenging. I have used this book and accompanying material in an online course at statistics.com effectively for several years.
October 17, 2008

Good combination on the practical application and the theory of Data Mining

I am now using this book for my graduate class. They like the book a lot. It has lots of interpretations on the reasons of variable selections, evaluation of the performances, advantages and weakness for each algorithm. These interpretations are based on lots of real examples. Further, with the complimentary XLMiner software, datasets, and case studies, the students can have hands-on assignments and projects with a real business questions.

Overall, I think this is a good combination of the practical application and theory of data mining.
October 15, 2008

Needs Serious Instructor Guidance

This book IS NOT for those who want to learn on their own. While this book does try to engage the learner by offering examples using storytelling, it fails on an instructional level. It doesn't fully explain the when or why behind the theory or application of data mining, and each aspect of it. It includes many equations that it just expects people to know. If you do know know orthogonal equations or linear regression by heart, this book will throw you for a loop.

I am taking an online class using this book, and I am ready to tear my hair out. Right now I am searching for books online to supplement my learning. If I find a better book, I will definitely add it to my post.
October 07, 2008

Excellent MBA/B-School Data Mining Book

I've used this as textbook for three years (even before it was in print) for my "Business Intelligence Using Data Mining" elective MBA course at the Indian School of Business. Till last Fall, I used to structure my class around the four major data-mining techniques explained well in this book; classification, prediction, clustering and association rules (what goes with what). The last time I switched completely to driving the class using the six or seven excellent cases at the back of the book, and the Business students loved that.

The cases and the associated data are rich; providing a business context to anchor the learning for students in the B-School. They allow the instructor to naturally cover important practical issues, such as over-sampling (when events that one is interested in -- say load defaults -- are rare), and asymmetric classification costs.

My class typically has a group project, where students have to pull everything together, from identifying a data mining opportunity, to collecting the data (beg, borrow or crawl:-), to performing exploratory data analysis (a key chapter in the book), to analyzing and presenting the results. Its usually more work than the students expect, but also typically much more learning than they expect.

In summary, a great resource for teaching the principles of data mining to anyone, and particularly useful for those in a Business School setting.
January 15, 2008

An Excellent Introduction, Works with Excel

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.
March 18, 2007

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