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Data preparation for data mining using SAS / Mamdouh Refaat.



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Refaat, Mamdouh.
Publication date:
Amsterdam ; Boston : Morgan Kaufmann Publishers, c2007.
  • Book
  • xxi, 399 p. : ill. ; 24 cm. + 1 CD-ROM (4 3/4 in.).
Includes bibliographical references (p. 373-374) and index.
  • 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.
  • (source: Nielsen Book Data)
Publisher's Summary:
Are you a data mining analyst, who spends up to 80 per cent 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. It 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; and, a unique and comprehensive approach for the treatment of missing values, optimal binning, and cardinality reduction. This title assumes minimal proficiency in SAS and includes a quick-start chapter on writing SAS macros. It features a CD that includes dozens of SAS macros plus the sample data and the program for the book's case study.
(source: Nielsen Book Data)
The Morgan Kaufmann series in data management systems
Morgan Kaufmann series in data management systems.

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