Data Mining Methods and Models (Hardcover)

by Daniel T. Larose (Author) "The databases typically used in data mining may have millions of records and thousands of variables..." (more)
Key Phrases: nutritional rating, churn data, scree plot criterion, Predictor Coef, International Plan, New York (more...)

Editorial Reviews

Review
"..the book is interesting to read, and the methods will be useful for data mining researchers…" (Computing Reviews.com, August 17, 2007)

"…an excellent problem-solving resource..." (CHOICE, June 2007)

"…the latest techniques…insight into how data mining algorithms work…" (Materials World, April 2007)

Product Description
Apply powerful Data Mining Methods and Models to Leverage your Data for Actionable Results

Data Mining Methods and Models provides:
* The latest techniques for uncovering hidden nuggets of information
* The insight into how the data mining algorithms actually work
* The hands-on experience of performing data mining on large data sets

Data Mining Methods and Models:
* Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing"
* Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises
* Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software
* Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes.

With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available onlne.

See all Editorial Reviews

Product Details

• Hardcover: 344 pages
• Publisher: Wiley-IEEE Press (January 30, 2006)
• Language: English
• ISBN-10: 0471666564
• ISBN-13: 978-0471666561
• Product Dimensions: 9.4 x 6.3 x 0.9 inches
• Shipping Weight: 1.2 pounds (View shipping rates and policies)
• Average Customer Review: 3.0 out of 5 stars See all reviews (1 customer review)
• Amazon.com Sales Rank: #701,440 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

1 of 1 people found the following review helpful:

3.0 out of 5 stars Strong on Statistics, Weaker on Data Mining, March 29, 2008

By	Keith McCormick (North Carolina, USA) - See all my reviews (REAL NAME)

This book is part of a Data Mining three book series. The first book is the strongest of the three, and should be considered first. My advice on this book is that if you 1) have a dozen or more related books, and 2) you have already worked on at least one or two data mining projects, you should buy this book. It has enough good material to add it to your collection. If you are new to data mining you should look elsewhere.

The other two books are: Discovering Knowledge in Data: An Introduction to Data Mining and Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage.

Methods and Models can be divided into three parts. The first part is a very strong 200 page review of traditional statistical techniques, specifically PCA/Factor Analysis, Multiple Regression, and Logistic Regression. These techniques absolutely belong in the Data Miners toolkit, but on most projects won't be as important as Decision Trees (covered in the first book). However, these are classical techniques, and the typical reader may have a lot of training or/and books on this material already. Having said that, this material is extremely well written. So if you are looking for a great review of these techniques you can do much worse. If you are looking for a discussion of how the application of these techniques to data mining differs from their application to statistics, you will be disappointed.

The second part of the book is the most helpful to me. It comprises of one chapter on Bayesian Networks and another on Genetic Algorithms. As always, the writing is clear, and to owners of SPSS Clementine 12.0, this section is of special interest because Bayesian Networks have just been added to Clementine. Methods and Models pre-dates the release of Clem 12.0, so it does not refer to it.

The third part of the book is a 50 page case study using the CRISP-DM methodology. This is a welcome addition, but is flawed in its execution. There are moments of brilliance: an application of over-balancing, a voting model, and tables showing several variations of models with their accuracies. Then ... the inevitable appearance in this book series of linear transformations and factor analysis as precursors to techniques that either do them automatically in Clementine, or are unneeded altogether. The veteran will know when to ignore this book, and when to pay attention- the novice might not.

In short, you should consider the first book, or both as a pair to add to a veteran data miner's collection. Only the rare reader should buy this book alone, and never as their first and only data mining book.