Data Mining Techniques Segmentation With Sas Enterprise Miner
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Data Mining Techniques Segmentation With Sas Enterprise Miner
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Author : Scientific Books
language : en
Publisher: CreateSpace
Release Date : 2015-05-08
Data Mining Techniques Segmentation With Sas Enterprise Miner written by Scientific Books and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-08 with categories.
SAS Institute implements data mining in Enterprise Miner software, which will be used in this book focused segmentation tasks. SAS Institute defines the concept of Data Mining as the process of selecting (Selecting), explore (Exploring), modify (Modifying), modeling (Modeling) and rating (Assessment) large amounts of data with the aim of uncovering unknown patterns which can be used as a comparative advantage with respect to competitors. This process is summarized with the acronym SEMMA which are the initials of the 5 phases which comprise the process of Data Mining according to SAS Institute. The essential content of the book is as follows: SAS ENTERPRISE MINER WORKING ENVIRONMENTSEGMENTATION PREDICTIVE TECHNIQUES MODELING PREDICTIVE TECHNIQUES FOR SEGMENTATION REGRESSION NODE: MULTIPLE REGRESSION MODEL LOGISTIC REGRESSION DMINE REGRESSION NODE SEGMENTATION PREDICTIVE TECHNIQUES. DECISION TREES DECISION TREE NODE DECISION TREE INTERACTIVE TRAINING DECISION TREE NODE OUTPUT DATA SOURCES GRADIENT BOOSTING NODE SEGMENTATION PREDICITIVE MODELS WITH NEURAL NETWORKS NEURAL NETWORKS FOR SEGMENTATION OPTIMIZATION AND ADJUSTMENT OF SEGMENTATION MODELS WITH NETS: NEURAL NETWORK NODE SIMPLE NEURAL NETWORKS PERCEPTRONS HIDDEN LAYERS MULTILAYER PERCEPTRONS (MLPS) RADIAL BASIS FUNCTION (RBF) NETWORKS LOCAL PROCESSING NETWORKS SCORING NEURAL NETWORK NODE TRAIN PROPERTIES NEURAL NETWORK NODE RESULTS AUTONEURAL NODE NETWORK ARCHITECTURES DM NEURAL NODE ENSEMBLE NODE SEGMENTATION DESCRIPTIVE TECHNIQUES. CLUSTER ANALYSIS CLUSTER ANALYSIS ON ENTERPRISE MINER CLUSTER NODE SOM/KOHONEN NODE VARIABLE CLUSTERING NODE PREDICTIVE MODELING WITH VARIABLE CLUSTERING EXAMPLE ASSESS PHASE IN SEGMENTATION PREDICTIVE MODELS CUTOFF NODE SCORE NODE SEGMENT PROFILE NODE
Customer Segmentation And Clustering Using Sas Enterprise Miner Third Edition
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Author : Randall S. Collica
language : en
Publisher: SAS Institute
Release Date : 2017-03-23
Customer Segmentation And Clustering Using Sas Enterprise Miner Third Edition written by Randall S. Collica and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-23 with Business & Economics categories.
Résumé : A working guide that uses real-world data, this step-by-step resource will show you how to segment customers more intelligently and achieve the one-to-one customer relationship that your business needs. --
Data Mining Techniques
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Author : Michael J. A. Berry
language : en
Publisher: John Wiley & Sons
Release Date : 2004-04-09
Data Mining Techniques written by Michael J. A. Berry 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 2004-04-09 with Business & Economics categories.
Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information.
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.
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.
Data Quality For Analytics Using Sas
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Author : Gerhard Svolba
language : en
Publisher: SAS Institute
Release Date : 2015-05-05
Data Quality 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 2015-05-05 with Computers categories.
Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba's Data Quality for Analytics Using SAS focuses on selecting the right data sources and ensuring data quantity, relevancy, and completeness. The book is made up of three parts. The first part, which is conceptual, defines data quality and contains text, definitions, explanations, and examples. The second part shows how the data quality status can be profiled and the ways that data quality can be improved with analytical methods. The final part details the consequences of poor data quality for predictive modeling and time series forecasting.
Commercial Data Mining
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Author : David Nettleton
language : en
Publisher: Elsevier
Release Date : 2014-01-29
Commercial Data Mining written by David Nettleton and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-29 with Computers categories.
Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling. Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book. - Illustrates cost-benefit evaluation of potential projects - Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools - Approachable reference can be read from cover to cover by readers of all experience levels - Includes practical examples and case studies as well as actionable business insights from author's own experience
Encyclopedia Of Data Warehousing And Mining Second Edition
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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2008-08-31
Encyclopedia Of Data Warehousing And Mining Second Edition written by Wang, John and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-31 with Computers categories.
There are more than one billion documents on the Web, with the count continually rising at a pace of over one million new documents per day. As information increases, the motivation and interest in data warehousing and mining research and practice remains high in organizational interest. The Encyclopedia of Data Warehousing and Mining, Second Edition, offers thorough exposure to the issues of importance in the rapidly changing field of data warehousing and mining. This essential reference source informs decision makers, problem solvers, and data mining specialists in business, academia, government, and other settings with over 300 entries on theories, methodologies, functionalities, and applications.
Advances In Data Mining Applications And Theoretical Aspects
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Author : Petra Perner
language : en
Publisher: Springer
Release Date : 2012-07-09
Advances In Data Mining Applications And Theoretical Aspects written by Petra Perner and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-09 with Computers categories.
This book constitutes the refereed proceedings of the 12th Industrial Conference on Data Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised full papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on data mining in medicine and biology; data mining for energy industry; data mining in traffic and logistic; data mining in telecommunication; data mining in engineering; theory in data mining; theory in data mining: clustering; theory in data mining: association rule mining and decision rule mining.