Data Mining Using Sas Enterprise Miner

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Data Mining Using Sas Enterprise Miner

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Author : Randall Matignon
language : en
Publisher: John Wiley & Sons
Release Date : 2007-08-13

Data Mining Using Sas Enterprise Miner written by Randall Matignon and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-13 with Mathematics categories.

The most thorough and up-to-date introduction to data mining techniques using SAS Enterprise Miner. The Sample, Explore, Modify, Model, and Assess (SEMMA) methodology of SAS Enterprise Miner is an extremely valuable analytical tool for making critical business and marketing decisions. Until now, there has been no single, authoritative book that explores every node relationship and pattern that is a part of the Enterprise Miner software with regard to SEMMA design and data mining analysis. Data Mining Using SAS Enterprise Miner introduces readers to a wide variety of data mining techniques and explains the purpose of-and reasoning behind-every node that is a part of the Enterprise Miner software. Each chapter begins with a short introduction to the assortment of statistics that is generated from the various nodes in SAS Enterprise Miner v4.3, followed by detailed explanations of configuration settings that are located within each node. Features of the book include: The exploration of node relationships and patterns using data from an assortment of computations, charts, and graphs commonly used in SAS procedures A step-by-step approach to each node discussion, along with an assortment of illustrations that acquaint the reader with the SAS Enterprise Miner working environment Descriptive detail of the powerful Score node and associated SAS code, which showcases the important of managing, editing, executing, and creating custom-designed Score code for the benefit of fair and comprehensive business decision-making Complete coverage of the wide variety of statistical techniques that can be performed using the SEMMA nodes An accompanying Web site that provides downloadable Score code, training code, and data sets for further implementation, manipulation, and interpretation as well as SAS/IML software programming code This book is a well-crafted study guide on the various methods employed to randomly sample, partition, graph, transform, filter, impute, replace, cluster, and process data as well as interactively group and iteratively process data while performing a wide variety of modeling techniques within the process flow of the SAS Enterprise Miner software. Data Mining Using SAS Enterprise Miner is suitable as a supplemental text for advanced undergraduate and graduate students of statistics and computer science and is also an invaluable, all-encompassing guide to data mining for novice statisticians and experts alike.

Introduction To Data Mining Using Sas Enterprise Miner

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Author : Patricia B. Cerrito
language : en
Publisher: SAS Press
Release Date : 2006

Introduction To Data Mining Using Sas Enterprise Miner written by Patricia B. Cerrito and has been published by SAS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Data mining categories.

"This manual provides a general, practical introduction to data mining using SAS Enterprise Miner and SAS Text Miner software"--Preface.

Neural Network Modeling Using Sas Enterprise Miner

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Author : Randall Matignon
language : en
Publisher: AuthorHouse
Release Date : 2005-08

Neural Network Modeling Using Sas Enterprise Miner written by Randall Matignon and has been published by AuthorHouse this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08 with Computers categories.

This book is designed in making statisticians, researchers, and programmers aware of the awesome new product now available in SAS called Enterprise Miner. The book will also make readers get familiar with the neural network forecasting methodology in statistics. One of the goals to this book is making the powerful new SAS module called Enterprise Miner easy for you to use with step-by-step instructions in creating a Enterprise Miner process flow diagram in preparation to data-mining analysis and neural network forecast modeling. Topics discussed in this book An overview to traditional regression modeling. An overview to neural network modeling. Numerical examples of various neural network designs and optimization techniques. An overview to the powerful SAS product called Enterprise Miner. An overview to the SAS neural network modeling procedure called PROC NEURAL. Designing a SAS Enterprise Miner process flow diagram to perform neural network forecast modeling and traditional regression modeling with an explanation to the various configuration settings to the Enterprise Miner nodes used in the analysis. Comparing neural network forecast modeling estimates with traditional modeling estimates based on various examples from SAS manuals and literature with an added overview to the various modeling designs and a brief explanation to the SAS modeling procedures, option statements, and corresponding SAS output listings.

Data Mining Using Sas Enterprise Miner

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Author : SAS Institute
language : en
Publisher: Sas Inst
Release Date : 2003

Data Mining Using Sas Enterprise Miner written by SAS Institute and has been published by Sas Inst this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.

Handbook Of Statistical Analysis And Data Mining Applications

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Author : Ken Yale
language : en
Publisher: Elsevier
Release Date : 2017-11-09

Handbook Of Statistical Analysis And Data Mining Applications written by Ken Yale and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-09 with Mathematics categories.

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Unknown Mir Title

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Author : SAS Publishing
language : en
Publisher:
Release Date : 2001-03

Unknown Mir Title written by SAS Publishing and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-03 with categories.

Data Preparation For Analytics Using Sas

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Author : Gerhard Svolba
language : en
Publisher: SAS Institute
Release Date : 2006-11-27

Data Preparation For Analytics Using Sas written by Gerhard Svolba and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-11-27 with Computers categories.

Written for anyone involved in the data preparation process for analytics, Gerhard Svolba's Data Preparation for Analytics Using SAS offers practical advice in the form of SAS coding tips and tricks, and provides the reader with a conceptual background on data structures and considerations from a business point of view. The tasks addressed include viewing analytic data preparation in the context of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations of data preparation for time series analysis, using various SAS procedures and SAS Enterprise Miner for scoring, creating meaningful derived variables for all data mart types, using powerful SAS macros to make changes among the various data mart structures, and more!

Data Mining And Predictive Analytics

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Author : Daniel T. Larose
language : en
Publisher: John Wiley & Sons
Release Date : 2015-03-16

Data Mining And Predictive Analytics written by Daniel T. Larose and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-16 with Computers categories.

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Statistical Data Mining Using Sas Applications

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Author : George Fernandez
language : en
Publisher: CRC Press
Release Date : 2010-06-18

Statistical Data Mining Using Sas Applications written by George Fernandez and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-18 with Business & Economics categories.

Statistical Data Mining Using SAS Applications, Second Edition describes statistical data mining concepts and demonstrates the features of user-friendly data mining SAS tools. Integrating the statistical and graphical analysis tools available in SAS systems, the book provides complete statistical data mining solutions without writing SAS program co

Decision Trees For Business Intelligence And Data Mining

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Author : Barry De Ville
language : en
Publisher: SAS Press
Release Date : 2006

Decision Trees For Business Intelligence And Data Mining written by Barry De Ville and has been published by SAS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Business & Economics categories.

This example-driven guide illustrates the application and operation of decision trees in data mining, business intelligence, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements other business intelligence applications.