Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner

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.

This book provides an excellent overview of a variety of data mining techniques related to business analysis. It does not provide much detailed statistical discussion or "how-to" steps. Instead, it provides enough detail to explain major concepts, the strengths and weaknesses of various analytic techniques, and when to use which technique. That alone is worth the price of admission. 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 is my first time taking a Data Mining course and this book so far, is beyond helpful. From examples throughout the book to the software tools of XLMiner, this book has taught me the fundamental tools that I needed in order to exceed in this course. XLMiner is easy to use and doesn't take that long for you to know the in's and out's of this tool. From the use of XLMiner, I have been able to understand and interpret my data in many ways. There are so many different models to test and compare your datasets all in one spot. This book is great for hands-on learning experience and applying real-life problem-solving skills.

To start of, I go to school at the University of New Haven, and this is the first year that this class is offered to students. The topic itself is confusing but interesting. It really takes time and practice to get to use all of the features that Data Mining and XLMINER have. With that said, this book is little to no help at all. The book greatly explains why you use each step involved and what each step does. What the book lacks is showing you how to do each step like partitioning data and using that data to make graphs. If it was not for my teacher, I would be lost in this class. I decided to give the book 3 stars because it is a somewhat easy book to read and it really helps you to understand why you use data mining and each of the things that it offers. I just wish it had better examples and walkthroughs on examples.

I am not a textbook reader; I often skim the chapters as I work through assignments. The textbook was great; information was pretty easy to locate. However, I believe it would have been helpful to add supplemental material that contains completed examples using XLMiner for each model. The documentation on XLMiner seemed to skim over often critical steps. I would question my confidence level of my results. Having the examples would give me something I could compare to.

Purchased this book for a data mining course.As a Finance major I needed a big refresher on concepts. This book is excellent as it it tailored for MBA students. Gives practical examples that are easy and clear to understand. I first rented this from Amazon but ended up purchasing to have around

While the book is not very technical, it gives a good introductory overview of common data mining techniques.

Great survey course style textbook. Gives a taste of a ton of data mining techniques in easy to read language. I plan to use this as a reference if I need to jog my memory on tasks or techniques.

Data Mining for Business Intelligence This highly academic, yet accessible and approachable book looks at practical techniques in data mining which can immediately be applied to ones working practices using Excel (and its plugin app - XLMiner, included as a six month license) The book contains the following sections, Preliminaries, including an introduction to Data Mining, data mining in business and a discussion of the different methods available. The authors then move on to give a full overview of the topic in the next chapter detailing the core ideas and also techniques for data reduction, exploration and visualisation. The rest of the book then expands upon these ideas with whole chapters devopted to: Data Exploration and Dimension Reduction Dimension Reduction Performace Evaluation Multiple Linear regression k-nearest Neighbours Naive Baynes Classification and Regression Trees Logistic regression Neural Nets Discriminant Analysis Associative Rules Clsuter analysis Handling Time Series Regression Based Forecasting Smoothing Methods. Finally, the book concludes with a number of case studies illustrating the above. Obviously a book of this type is going to appeal to a narrow band of specialists either in academic or industry who need to use these particular techniques in their work. Speaking as an IT professional however who has a number of Data Warehouse projects under his belt, I have found that this work is an ideal introduction and guide in this field and will contain lots of techniques which can be easily applied to work.

I've tried to read through this book, but I only got it for interest, not linked to my job or any particular project. To be honest I've found it heavy going, as the content is dense and I think that it assumes some foreknowledge that I do not have - though I'm hard pressed to be specific on that. I think if I was immersed in database management or business management and had a particular data mining project in mind, I would be motivated to press on with the book - as I'm sure that I would find everything I need within it's pages to get me through. However, as a semi-casual reader wanting an overview of the subject I find the book unsuitable for me. It's well written, seems to be comprehensive and comes with good support (software and online links), so if you have better motivation than me I'm sure the book will deliver for you. Alan T

This book is aimed at business students and does require specialist knowledge gathered on a statistics course and picked up through the use of excel or other spreadsheet programs that allow you to capture a lot of data. As the book is called "data-mining" the attempt is to enable readers to practically sort data through techniques to increase the relevance of the data capture. This is done very successfully through steps starting with understanding the data need, cleaning it up, process of choosing algorithms and essentially other best practice steps that would help a beginner and cement the process in those with prior experience in mining data for projects. I stress that this isn't particularly easy to jump into if you don't have the prior statistical background. There is certainly enough information to learn the steps and answer the questions at the end of each chapter, but a significant proportion of the book would be best supplemented by doing a prior statistics course so you are familiar with terms such as multiple regression, multicollinearity and homoscedasticity. If you can't define one of these terms (helpfully refreshed in book), then you may find this read a struggle. Those on a statistics course or having completed one will find it useful/supplementary to the knowledge they learn in theoretical courses being put into practice. I haven't tried the XL software yet. Preferring to wait till a time I can get the most use out of the 6 month license.

Data Mining for Business Intelligence (Second Edition) is written for analysts and professionals engaged in business intelligence statistical analysis and data mining, as well as for students specialising in this field. It is comprehensive and well written. True, readers would benefit from prior mathematical knowledge, although the authors do a good job of explaining and guiding the reader through the mathematical equations that are necessary for this field. However, for me the book loses a couple of stars through only supplying a six month licence for XLMiner, which is needed to fully work through many of the book's examples. Although many professionals will have their own analysis software, other readers will find the book's usefulness devalued slightly after the licence expires unless they purchase a full licence. A pity the publishers don't supply readers with a free version for non-commercial use.

Whilst well written, this book is complex and highly technical. Make sure before buying it that you are choosing the book that matches your need. It is very specialist and not intended as an introduction. I choose this book from the Amazon Vine programme for my husband as it is useful in his workplace and he did say it was useful if you were coming to it from an application point of view and not general interest.