- Hardcover: 428 pages
- Publisher: Wiley; 2 edition (October 26, 2010)
- Language: English
- ISBN-10: 0470526823
- ISBN-13: 978-0470526828
- Product Dimensions: 7.3 x 1.1 x 10.3 inches
- Shipping Weight: 2 pounds (View shipping rates and policies)
- Average Customer Review: 61 customer reviews
- Amazon Best Sellers Rank: #226,225 in Books (See Top 100 in Books)
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Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner 2nd Edition
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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 Naïve 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.
Top customer reviews
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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.
The book works in conjunction with a software called DataMiner XL and the questions asked in the chapters relate to this software. The problem is that the book asks you to perform complex tasks, but never goes ahead to explain how to do them using the software. It is up to you to do external research in order to find out how to complete the tasks. I believe that an instructional book such as this one should offer step by step guidance on the processes in which they expect you to perform.
I would not recommend this book to anybody based on these reasons. The reason it got 2 stars is because the concepts were explained decently well.
Readers without some background in math or statistics may find it necessary to do additional reading if they want to implement these techniques on their own. The book was originally intended to support college level teaching, where this kind of background is acquired in the class room and in study groups. Readers who buy this book for independent study may be disappointed that the answer key to the study problems is only available to instructors. A shame, really, since the authors clearly put a great deal of effort into finding many excellent case studies.
Overall, I found this book worth the investment of my time and money. It provides an excellent outline for determining an analytics approach to most business questions.
This book does a great job with overviews. By that, I mean it explains a general topic fairly well. However, when it comes down to "how to" steps, the book is of almost no help. If there are "how-to" guides, they are very poorly written and extremely hard to follow. For most topics, it simply compares and contrasts different how to methods without actually explaining how to do them.