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Data preparation for data mining using SAS [electronic resource] / Mamdouh Refaat.

By: Refaat, Mamdouh.
Material type: TextTextSeries: Morgan Kaufmann series in data management systems: Publisher: Amsterdam ; Boston : Morgan Kaufmann Publishers, c2007Description: 1 online resource (xxi, 399 p.) : ill.ISBN: 9780080491004 (electronic bk.); 0080491006 (electronic bk.).Subject(s): Data mining | SAS (Computer file) | COMPUTERS -- Database Management -- Data MiningGenre/Form: Electronic books.Additional physical formats: Print version:: Data preparation for data mining using SAS.DDC classification: 006.3/12 Online resources: EBSCOhost
Contents:
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.
Summary: 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.
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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.

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.

Description based on print version record.

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Data preparation for data mining using SAS by Refaat, Mamdouh. ©2007
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