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Data Mining Using SAS Enterprise Miner
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ISBN
:
9788126535668
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Publisher
:
Wiley India Pvt Ltd
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Subject
:
Mathematics
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Binding
:
Paperback
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Pages
:
564
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Year
:
2012
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929.0
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761.0
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Description
Data Mining Using SAS Enterprise Miner(TM) introduces the reader to a wide variety of data mining techniques in SAS Enterprise Miner (TM). This first-of-a-kind book explains the purpose of -- and reasoning behind -- every node that is a part of Enterprise Miner with regard to SEMMA design and data mining analysis. Each chapter starts with a short introduction to the assortment of statistics that are generated from the various SAS Enterprise Miner (TM) nodes, followed by detailed explanations of configuration settings that are located within each node. The end result of the author's meticulous presentation is a well crafted study guide on the various methods that one employs to both randomly sample and partition data within the process flow of SAS Enterprise Miner (TM). Special Features The emphasis of the book is to explore node relationships and patterns by using data from an assortment of computations, charts, and graphs so common in SAS procedures. Particular attention is paid to the wide variety of statistical techniques that can be performed from the Insight, modify, modeling, and assessment nodes. The Score node and SAS code are described in detail, thus showcasing the importance of managing, editing, executing, and creating custom-designed codes for the benefit of fair and comprehensive business decision making. A step-by-step approach to each node discussion is employed, along with an assortment of illustrations, in order to familiarize the reader with the somewhat complex and confusing working environment in SAS Enterprise Miner (TM). The author maintains a web site that provides further insights into the discussions in the book, as well as downloadable data sets for implementation, manipulation, and interpretation. About The Author Randall Matignon is Senior Clinical SAS/Microsoft Office VBA Programmer for Amgen in San Francisco (CA). He has over ten years of experience in automating pharmaceutical, health care and biotechnical company analysis process by linking SAS and Microsoft Office Excel/Word/Access/Powerpoint VBA applications to menu-driven statistical analysis applications designed from client specific requests. He is the author of Neural Network Modeling Using SAS Enterprise Miner, published in 2005. He has extensive knowledge of several programming languages ranging from S-Plus to PL-SQL. Table of Contents Introduction Sample Nodes Explore Nodes Modify Nodes Model Nodes Assess Nodes Scoring Nodes Utility Nodes References Index
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