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Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data.
From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization.
The Second Edition now features:
- Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles
- A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice
- Separate chapters that each treat k-nearest neighbors and Na�ve Bayes methods
- Summaries at the start of each chapter that supply an outline of key topics
Data Mining for Business Intelligence, Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
- Sales Rank: #39575 in Books
- Published on: 2010-10-26
- Original language: English
- Number of items: 1
- Dimensions: 10.30" h x 1.10" w x 7.30" l, 2.04 pounds
- Binding: Hardcover
- 428 pages
Review
"full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."
--Research�magazine
"Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature."
--computingreviews.com
"The book would be useful for a one- or two-semester data mining course or a business intelligence course." (The American Statistician, 1 November 2011)
�
From the Back Cover
Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data.
From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization.
The Second Edition now features:
- Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles
- A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice
- Separate chapters that each treat k-nearest neighbors and Na�ve Bayes methods
- Summaries at the start of each chapter that supply an outline of key topics
Data Mining for Business Intelligence, Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
About the Author
GALIT SHMUELI, PhD, is Associate Professor of Statistics and Director of the eMarkets Research Lab in the Robert H. Smith School of Business at the University of Maryland. Dr. Shmueli is the coauthor of Statistical Methods in e-Commerce Research and Modeling Online Auctions, both published by Wiley.
NITIN R. PATEL, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology for over ten years.
PETER C. BRUCE is President and owner of statistics.com, the leading provider of online education in statistics.
Most helpful customer reviews
20 of 20 people found the following review helpful.
Excellent choice for business analysts that want a brief exposure with case studies (and software)
By Keith McCormick
I am a data mining trainer and consultant. This book not only has good content, but it offers a 90 day license of software with which to rehearse the case study examples. My comments on the book will be accompanied by comments on the software. The book is the perfect fit for its intended audience. With the caution that certain readers will do better elsewhere, I think it is a great book. The major topics are addressed, albeit briefly, with clarity. If you are a first timer reader of this subject, there are not many books that will do a better job explaining these technical subjects for a general audience.
Like most full time data miners, I would have difficulty living within the constraints of Excel. XLMiner is a fine piece of software, but it lives inside Excel as an Excel add-on. The most famous limitation is having no more than 1,000,000 rows of data, but that nature of that limitation applied to Data Mining is frequently misunderstood. I am often on projects with "big data" clients where I only model 100,000 or fewer records. XLMiner allows you to read from a database larger than Excel can handle, and let's you write out to a database larger than Excel can handle. I was surprised and impressed by this. In the end, though, it still isn't enough. I need to be able to merge and manipulate my large data files so that I can carefully select the smaller fraction that I am going to model. In short, I can't live without my more powerful tools. There is an essay offered as a sidebar in the book on the state of the Data Mining Software Tools market by Herb Edelstein which discusses exactly this fact. XLMiner was originally developed as a piece of teaching software, and it excels at that. It doesn't intend to be a deployment tool for the whole business enterprise like some of the more powerful Data Mining suites. If you don't have access to such tools you might be pleasantly surprised what it can do since the other tools are many times more expensive.
Despite this limitation, this is a strong book. It would be just perfect for MBAs that are intrigued with Data Mining. It would be great for a first course in Data Mining provided that it wasn't the first of many. If someone were about to embark on a Data Mining advanced degree, I don't think this book is the best route to go. I would suggest Handbook of Statistical Analysis and Data Mining Applications as an introduction for that audience. I also think it is an outstanding choice for a seminar leader that wants to offer demonstrations for the audience. I would suggest providing the audience with copies (or allowing them to get them). What a great way to learn the material - by doing. I debated using this book for exactly that purpose and ended up going with the Handbook of Statistical Analysis and Data Mining Applications only because I felt my audience, representing larger companies, would end up using one the Data Mining suites in the end, and I wanted them to see them.
I would also suggest this book for self study. It is as easy a read as this kind of material is going to get. Technical? Yes. Light reading? Not really. However, Data Mining algorithms never make for light reading. What you hope for is clarity, and the right amount of detail. For the uninitiated, this is perfect. For Data Mining professionals, it would be just a very basic review. Some reviewers seems to have found it a tough slog. It is very much in the style of "here is the rough idea - try a case study". If you've never studied statistics, there is no careful walk through of the formulas, but that is not the point of the book. Lots of other books do that. If you want to know how Data Mining works "under the hood" you won't really find that here either. For example, Regression is covered in about 15 pages. Overall, I think it makes good choices in terms of detail.
It covers all the material you need in an introduction. It offers a very brief initial chapter defining the subject. It does a decent job at data visualization. It is a basic introduction the algorithms with supporting case studies. The is almost no data preparation because XLMiner is not designed to do any heavy lifting here. It can do partitioning and explains why this is critical to data mining. For a good discussion of data preparation and Excel read Linoff's fine book Data Analysis Using SQL and Excel. A surprising number of the famous techniques are here: neural nets, k nearest neighbors, clustering, classification trees and even time series analysis. The case studies are fairly basic, but well described. They are easy to download from the website. Again, perfect for a first course in Data Mining. Everything an instructor would need for a good solid introduction - exactly the audience the book was written for.
36 of 40 people found the following review helpful.
Useless
By Amazon Customer
Pretty shallow presentation of very basic statistical algorithms. Algorithms presented without any explanation of mathematical backgrounds and assumptions when they can be used. Almost no formulas in text, just data and plots. These algorithms are standard statistical algorithms; it is not clear from the text whether "data mining" and "statistics" is the same or not.
What is worse, there is Excel library that must be used with the book. All examples are in the context of this library. LICENSE LASTS ONLY 6 MONTH. Means, after 6 month you can put quite expensive ($100) book in trash. Or spend few thousand bucks for full license, what taking into account the "sophistication" of the library would be wasting of money. Take R language that is free and infinitely more powerful.
Warning if you purchase used book: each copy of the book has unique license ID that must be used to download the software. Once downloaded, this software cannot be downoaded second time. This means that it is not possible to download software if you purchased used book
10 of 10 people found the following review helpful.
Good Introductory Text
By Robert L. Phillips
I plan to use this book as a supplement to an MBA-level course on Business Analytics. It is extremely well organized, clearly written and introduces all of the basic ideas quite well. It covers the fundamentals of "data mining" and "data visualization", classification and prediction, logistic regression, cluster analysis, and forecasting. It comes with a 6 month license to XLMiner which is an add-in to Excel and access to data for a number of cases.
So why not 5 stars? Two reasons. First of all, there are an incredible number of typos in the book -- far too many for a Second Edition. Some of these could have been caught by simple spell check -- thus, "dimention" for "dimension" and "Ecploration" for "Exploration" -- both on Page 9. Others seem to have been inserted by gremlins -- thus, the characters "Kare" are insterted in the first sentence on Page 12 for no obvious reason.
Secondly, I found the XLMiner software to be clunky and difficult to use. On a test data set that I have used for a number of purposes, XLMiner Classification Tree was unable to find any split whereas Salford's CART program easily found splits. Usage is unintuitive. For this reason, I will probably not use the add-in in the course.
Also, as a very small nit, I disagreed with the book's treatment of missing data. They suggesting imputing values or eliminating records with missing data. In my experience, it is more important to understand why data is missing -- it is usually a process issue and imputing values or eliminating records can add significant bias to the analysis. But this is more of a quibble. On the whole, this is an excellent book and the best overall introduction to the topic that I have found.
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