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Data preparation for data mining using SAS

紀錄類型:	書目-電子資源 : 單行本
正題名/作者:	Data preparation for data mining using SAS/ Mamdouh Refaat.
作者:	Refaat, Mamdouh.
出版者:	Amsterdam ;Morgan Kaufmann Publishers, : c2007.,
面頁冊數:	xxi, 399 p. :ill. ; : 24 cm. +; 1 CD-ROM (4 3/4 in.). +
叢書名:	The Morgan Kaufmann series in data management systems
標題:	Data mining. -
電子資源:	An electronic book accessible through the World Wide Web; click for information
ISBN:	9780123735775

Data preparation for data mining using SAS
Refaat, Mamdouh.

Data preparation for data mining using SAS[electronic resource] /Mamdouh Refaat. - Amsterdam ;Morgan Kaufmann Publishers,c2007. - xxi, 399 p. :ill. ;24 cm. +1 CD-ROM (4 3/4 in.). - The Morgan Kaufmann series in data management systems.

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.

Electronic reproduction.
Amsterdam :
Elsevier Science & Technology,
2007.

Mode of access: World Wide Web.

ISBN: 9780123735775

Source: 135067:135202Elsevier Science & Technologyhttp://www.sciencedirect.comSubjects--Uniform Titles:

SAS (Computer file)
Subjects--Topical Terms:

88799
Data mining.
Index Terms--Genre/Form:

96803
Electronic books.

LC Class. No.: QA76.9.D343 / R43 2007eb

Dewey Class. No.: 006.3/12

Data preparation for data mining using SAS
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505 0 $a 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.
520 $a 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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