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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.

Reviews

"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 find?a 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, Cormine Intelligent Data, LLC

Feature

• 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.

정보제공 :

Contents 
1 Introduction 
2 Tasks and Data Flow 
3 Review of Data Mining Modeling Techniques 
4 SAS Macros: A Quick Start 
5 Data Acquisition and Integration 
6 Integrity Checks 
8 Sampling and Partitioning 
9 Data Transformations 
10 Binning and Reduction of Cardinality
11 Treatment of Missing Values 
12 Predictive Power and Variable Reduction I 
13 Analysis of Nominal and Ordinal Variables 
14 Analysis of Continuous Variables 
15 Principal Component Analysis (PCA) 2 
16 Factor Analysis 
17 Predictive Power and Variable Reduction II 
18 Putting it All Together 
A Listing of SAS Macros

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