Data preparation for data mining using SAS [electronic resource] / Mamdouh Refaat.
- 作者： Refaat, Mamdouh.
- 其他作者：
- 其他題名：
- The Morgan Kaufmann series in data management systems
- 出版： Amsterdam ;Boston : Morgan Kaufmann Publishers c2007.
- 叢書名： The Morgan Kaufmann series in data management systems
- 主題： Data mining. , Electronic books , SAS (Computer file)
- ISBN： 0123735777 、 9780123735775
- URL：
An electronic book accessible through the World Wide Web; click for information
- 一般註：Electronic reproduction. Amsterdam : Elsevier Science & Technology, 2007.
- 書目註：Includes bibliographical references (p. 373-374) and index.
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讀者標籤：
- 系統號： 005163762 | 機讀編目格式
館藏資訊
摘要註
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.
內容註
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.