Introduction To Data Mining Using Sas Enterprise Miner
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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.
Data Mining Using Sas Enterprise Miner
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Author : Randall Matignon
language : en
Publisher: John Wiley & Sons
Release Date : 2007-08-03
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 2007-08-03 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.
Business Analytics Using Sas Enterprise Guide And Sas Enterprise Miner
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Author : Olivia Parr-Rud
language : en
Publisher: SAS Institute
Release Date : 2014-10-01
Business Analytics Using Sas Enterprise Guide And Sas Enterprise Miner written by Olivia Parr-Rud and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-01 with Computers categories.
This tutorial for data analysts new to SAS Enterprise Guide and SAS Enterprise Miner provides valuable experience using powerful statistical software to complete the kinds of business analytics common to most industries. Today’s businesses increasingly use data to drive decisions that keep them competitive. Especially with the influx of big data, the importance of data analysis to improve every dimension of business cannot be overstated. Data analysts are therefore in demand; however, many hires and prospective hires, although talented with respect to business and statistics, lack the know-how to perform business analytics with advanced statistical software. Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner is a beginner’s guide with clear, illustrated, step-by-step instructions that will lead you through examples based on business case studies. You will formulate the business objective, manage the data, and perform analyses that you can use to optimize marketing, risk, and customer relationship management, as well as business processes and human resources. Topics include descriptive analysis, predictive modeling and analytics, customer segmentation, market analysis, share-of-wallet analysis, penetration analysis, and business intelligence. This book is part of the SAS Press program.
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.
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.
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 Computers categories.
Understanding your customers is the key to your company’s success! Segmentation is one of the first and most basic machine learning methods. It can be used by companies to understand their customers better, boost relevance of marketing messaging, and increase efficacy of predictive models. In Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition, Randy Collica explains, in step-by-step fashion, the most commonly available techniques for segmentation using the powerful data mining software SAS Enterprise Miner. A working guide that uses real-world data, this new edition will show you how to segment customers more intelligently and achieve the one-to-one customer relationship that your business needs. Step-by-step examples and exercises, using a number of machine learning and data mining techniques, clearly illustrate the concepts of segmentation and clustering in the context of customer relationship management. The book includes four parts, each of which increases in complexity. Part 1 reviews the basics of segmentation and clustering at an introductory level, providing examples from a variety of industries. Part 2 offers an in-depth treatment of segmentation with practical topics, such as when and how to update your models. Part 3 goes beyond traditional segmentation practices to introduce recommended strategies for clustering product affinities, handling missing data, and incorporating textual records into your predictive model with SAS Text Miner. Finally, part 4 takes segmentation to a new level with advanced techniques, such as clustering of product associations, developing segmentation-scoring models from customer survey data, combining segmentations using ensemble segmentation, and segmentation of customer transactions. New to the third edition is a chapter that focuses on predictive models within microsegments and combined segments, and a new parallel process technique is introduced using SAS Factory Miner. In addition, all examples have been updated to the latest version of SAS Enterprise Miner.
Data Mining With Sas Enterprise Miner Through Examples
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Author : Cesar Lopez
language : en
Publisher: CreateSpace
Release Date : 2013-06-26
Data Mining With Sas Enterprise Miner Through Examples written by Cesar Lopez and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-26 with categories.
This book presents the most common techniques used in data mining in a simple and easy to understand through one of the most common software solutions from among those existing in the market, in particular, SAS ENTERPRISE MINER. Pursued as initial aim clarifying the applications concerning methods traditionally rated as difficult or dull. It seeks to present applications in data mining without having to manage high mathematical developments or complicated theoretical algorithms, which is the most common reason for the difficulties in understanding and implementation of this matter. Today data mining is used in different fields of science. Noteworthy applications in banking, and financial analysis of markets and trade, insurance and private health, in education, in industrial processes, in medicine, biology and bioengineering, telecommunications and in many other areas. Essentials to get started in data mining, regardless of the field in which it is applied, is the understanding of own concepts, task that does not require nor much less the domain of scientific apparatus involved in the matter. Later, when either necessary operative advanced, computer programs allow the results without having to decipher the mathematical development of the algorithms that are under the procedures. This book describes the simplest possible data mining concepts, so that they are understandable by readers with different training. The chapters begin describing the techniques in affordable language and then presenting the way to treat them through practical applications. An important part of each chapter are case studies completely resolved, including the interpretation of the results, which is precisely the most important thing in any matter with which they work. The book begins with an introduction to mining data and its phases. In successive chapters develop the initial phases (selection of information, data exploration, data cleansing, transformation of data, etc.). Subsequently elaborates on specific data mining, both predictive and descriptive techniques. Predictive techniques covers all models of regression, discriminant analysis, decision trees, neural networks and other techniques based on models. The descriptive techniques vary dimension reduction techniques, techniques of classification and segmentation (clustering), and exploratory data analysis techniques.
Decision Trees For Analytics Using Sas Enterprise Miner
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Author : Barry De Ville
language : en
Publisher:
Release Date : 2019-07-03
Decision Trees For Analytics Using Sas Enterprise Miner written by Barry De Ville and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-03 with Computers categories.
Decision Trees for Analytics Using SAS Enterprise Miner is the most comprehensive treatment of decision tree theory, use, and applications available in one easy-to-access place. This book illustrates the application and operation of decision trees in business intelligence, data mining, 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 data mining approaches such as regression, as well as other business intelligence applications that incorporate tabular reports, OLAP, or multidimensional cubes. An expanded and enhanced release of Decision Trees for Business Intelligence and Data Mining Using SAS Enterprise Miner, this book adds up-to-date treatments of boosting and high-performance forest approaches and rule induction. There is a dedicated section on the most recent findings related to bias reduction in variable selection. It provides an exhaustive treatment of the end-to-end process of decision tree construction and the respective considerations and algorithms, and it includes discussions of key issues in decision tree practice. Analysts who have an introductory understanding of data mining and who are looking for a more advanced, in-depth look at the theory and methods of a decision tree approach to business intelligence and data mining will benefit from this book.
Data Mining Using Enterprise Miner Software
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Author : SAS Institute
language : en
Publisher: SAS Press
Release Date : 2000-01-01
Data Mining Using Enterprise Miner Software written by SAS Institute and has been published by SAS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01-01 with Computers categories.
This introductory guide uses a case study approach to take you through the Enterprise Miner interface from initial data access to a completed association analysis. If you are a new Enterprise Miner user, you will find this guide to be an invaluable resource as you navigate the interface. After completing the case study in this guide, you will be prepared to tackle the more complicated statistical analyses that are covered in the Enterprise Miner online reference documentation. This title is available for purchase as a hardcopy book.
Sas Data Mining
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Author : Cesar Perez Lopez
language : en
Publisher: Apress
Release Date : 2014-12-17
Sas Data Mining written by Cesar Perez Lopez and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-17 with Computers categories.
Data mining is used today in many different fields including banking, financial analysis of markets, insurance and private health sectors, education, industrial processes, medicine, biology, bioengineering and telecommunication. But regardless of the field in which it is applied, the core concepts and tasks of data mining do not require nor domain-specific knowledge, nor advanced mathematical treatments. SAS Data Mining presents the most common techniques used in SAS data mining in a simple and easy to understand way using SAS Enterprise Miner, regardless of the specific field you're working in, and without needing to draw on complicated mathematical algorithms. SAS Data Mining therefore describes data mining techniques to you in accessible language and clear, practical, hands-on examples and exercises. Each chapter presents a data mining case study, including the results of the case study, you've built and an interpretation of its results, which is so vital of course to your data mining work. SAS Data Mining begins with an introduction to data mining data and its distinct phases. You'll then learn how to develop the initial phases which include the selection of information, data exploration, data cleansing, transformation of data, and related issues. After these initial data mining phases, this book goes into practical, hands-on detail on both predictive and descriptive data mining techniques. The predictive techniques you'll learn about cover regression, discriminant analysis, decision trees, neural networks, and other model-based techniques. The descriptive techniques then work with variable dimension reduction techniques, techniques of classification and segmentation (clustering), and exploratory data analysis techniques. What you’ll learn Core concepts of data mining, which can apply to whichever domain of data mining you're working in Practical, hands-on examples and exercises of data mining techniques using SAS Data Miner How to conduct initial phase information selection, data exploration, and data cleansing Transforming your initial data selection as appropriate for your data situation About both predictive and descriptive data mining techniques How to use models of regression, discriminant analysis, decision trees, neutral networks and other model-based techniques Variable dimension reduction techniques, and techniques of classification and segmentation (clustering) How to use exploratory data analysis techniques Who this book is for This book is suitable for IT, MIS and data analysis professionals who want to learn to use SAS Enterprise Miner. Database professionals and other technologists who are looking to either get into the area, are new to Enterprise Miner or are making decisions on data analysis technology adoption will also benefit from this book.