Data Preparation for Data Mining Using SAS (The Morgan Kaufmann...

Features

• Cover Type: Paperback with 424 pages
• Published by: Morgan Kaufmann; Pap/Cdr edition September 29, 2006
• Written in: English
• ISBN 10 Number: 0123735777
• ISBN 13 Number: 978-0123735775
• Book Dimensions: 9 x 7.4 x 1.1 inches
• Weighs: 2 pounds

Product Review
It is easy to write books that address broad topics and ideas leaving the reader with the question Yes, but how? By combining a comprehensive guide to data preparation for data mining along with specific examples in SAS, Mamdouh's book is a rare finda blend of theory and the practical at the same time. As anyone who has mined data will confess, 80% of the problem is in data preparation; Mamdouh addresses this difficult subject with strong practical techniques and methods.

If you are working on an SAS data mining project, this book is a must! If you are working on any data mining project, the techniques and methods will be a guiding light! --Frank Byrum, Chief Scientist, CorMine Intelligent Data, LLC

Book Description
Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little how to information? And are you, like most analysts, preparing the data in SAS?

This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection.

FEATURES
* A complete framework for the data preparation process, including implementation details for each step.
* The complete SAS implementation code, which is readily usable by professional analysts and data miners.
* A unique and comprehensive approach for the treatment of missing values, optimal binning, and cardinality reduction.
* Assumes minimal proficiency in SAS and includes a quick-start chapter on writing SAS macros.
* CD includes dozens of SAS macros plus the sample data and the program for the book's case study.

Reader Reviews
It is well known that the majority of time spent doing data mining is in the data preparation. With this book, Dr. Refaat has given analysts the tools they need to perform the often laborious task of data preparation. If you are familiar with the Numerical Recipes series of books (and if you aren't, you should be), you will recognize a similar theme. The author has: * itemized all the elements the data miner should be aware of when they are doing data preparation, * presented enough technical description for the reader to understand why they would be performing that particular task, * provided all the SAS code that can be used to actually perform the data preparation step This book is what the practicing data miner needs. Here's a brief on the table of contents: 1. Introduction * setting the context of data mining 2. Tasks and Data Flow * describes what data mining can do and where data preparation fits in 3. Review of Data Mining Modeling Techniques * an overview of data mining techniques 4. SAS Macros: A Quick Start * just in case you haven't worked with SAS macros 5. Data Acquisition and Integration * where you get your data from and how it's pulled together 6. Integrity Checks * how to make sure the data is correct and even what "correct" means 7. Exploratory Data Analysis * get to know your data 8. Sampling and Partition * dealing with large data sets as well as getting ready to validate the models you build 9. Data Transformations * rarely is your source data in the form most effective for modeling - this chapter describes what can be done to produce the most effective models 10. Binning and Reduction of Cardinality * make your variables less complex and often times, more presentable and understandable 11. Treatment of Missing Values * you will have missing values in your data - here are several approaches for dealing with them 12. Predictive Power and Variable Reduction I * introduces the concept of identifying usefulness of input variables and reducing the required number of variables 13. Analysis of Nominal and Ordinal Variables * how to evaluate relationships with discrete variables 14. Analysis of Continuous Variables * how to evaluate relationships with continuous variables 15. Principal Component Analysis * how to use PCA for variable reduction during data preparation 16. Factor Analysis * how to use Factor Analysis for variable reduction during data preparation 17. Predictive Power and Variable Reduction II * defines methods of simplifying and reducing input variables with respect to the target variable 18. Putting It All Together * a case study showing the application of all these techniques for data preparation in a realistic example Appendix. Listing of SAS Macros * complete listing of all the SAS code referenced in the book - also included on the CD Comment | | (Report this)