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Author : Patricia B. Cerrito
Publisher : SAS Press
Page : 0 pages
File Size : 46,7 Mb
Release : 2006
Category : Data mining
ISBN : 1590478290
"This manual provides a general, practical introduction to data mining using SAS Enterprise Miner and SAS Text Miner software"--Preface.
Author : Randall Matignon
Publisher : John Wiley & Sons
Page : 584 pages
File Size : 43,7 Mb
Release : 2007-08-03
Category : Mathematics
ISBN : 9780470149010
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.
Author : SAS Institute
Publisher : Sas Inst
Page : 127 pages
File Size : 50,5 Mb
Release : 2003
Category : Computers
ISBN : 1590471903
Author : SAS Publishing
Publisher : Unknown
Page : 128 pages
File Size : 53,8 Mb
Release : 2001-03
Category : Electronic
ISBN : 1590477383
Author : Olivia Parr-Rud
Publisher : SAS Institute
Page : 231 pages
File Size : 42,5 Mb
Release : 2014-10-01
Category : Computers
ISBN : 9781629593289
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.
Author : Kattamuri S. Sarma
Publisher : SAS Institute
Page : 574 pages
File Size : 49,5 Mb
Release : 2017-07-20
Category : Computers
ISBN : 9781635260403
« Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. »--
Author : Barry De Ville
Publisher : SAS Press
Page : 224 pages
File Size : 53,9 Mb
Release : 2006
Category : Business & Economics
ISBN : 1590475674
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.
Author : Tim Rey ,Arthur Kordon,Chip Wells
Publisher : SAS Institute
Page : 336 pages
File Size : 51,9 Mb
Release : 2012-07-02
Category : Computers
ISBN : 9781612900933
Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast variable. This book is essential for forecasting practitioners who need to understand the practical issues involved in applied forecasting in a business setting. Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs. This book is part of the SAS Press program.
Author : Cesar Lopez
Publisher : CreateSpace
Page : 356 pages
File Size : 48,6 Mb
Release : 2013-06-26
Category : Electronic
ISBN : 1490541799
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.
Author : Randall S. Collica
Publisher : SAS Institute
Page : 356 pages
File Size : 46,5 Mb
Release : 2017-03-23
Category : Computers
ISBN : 9781629605272
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.
Author : Dr. Goutam Chakraborty,Murali Pagolu,Satish Garla
Publisher : SAS Institute
Page : 340 pages
File Size : 41,9 Mb
Release : 2014-11-22
Category : Computers
ISBN : 9781612907871
Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries. Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis. This book is part of the SAS Press program.
Author : Robert Nisbet,Gary Miner,Ken Yale
Publisher : Elsevier
Page : 822 pages
File Size : 43,6 Mb
Release : 2017-11-09
Category : Mathematics
ISBN : 9780124166455
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
Author : SAS Institute
Publisher : Sas Inst
Page : 76 pages
File Size : 47,5 Mb
Release : 2009
Category : Computers
ISBN : 1599943212
Introduces the core functionality of SAS Enterprise Miner and shows how to perform basic data-mining tasks. Provides step-by-step examples that create a complete process-flow diagram, including graphic results.
Author : SAS Institute
Publisher : SAS Institute
Page : 147 pages
File Size : 42,9 Mb
Release : 2006
Category : Computers
ISBN : 1599940027
SAS Enterprise Miner 5.2 is the SAS data mining solution that addresses the entire data mining process using an intuitive Java point-and-click interface. This guide introduces you to the core functionality of SAS Enterprise Miner and shows you how to perform basic data mining tasks. You will learn how to use the graphical user interface (GUI) tools to create and manage process flow diagrams and projects, and to export mining results for reporting and integration with other SAS software. The data mining tasks you will learn include sampling, exploring, modifying, modeling, and assessing data in order to create and refine predictive models. Getting Started with Enterprise Miner 5.2 provides step-by-step examples that create a complete process flow diagram, including graphic results. This title is also available online. This title is intended for statisticians, quantitative analysts, and business technologists who want to learn to use the data mining capabilities of SAS Enterprise Miner.
Author : SAS Institute
Publisher : Unknown
Page : 82 pages
File Size : 47,5 Mb
Release : 2013-12
Category : Mathematics
ISBN : 1612905528
Introduces the core functionality of SAS Enterprise Miner and shows how to perform basic data mining tasks. Provides step-by-step examples that create a complete process-flow diagram including graphic results. Introduces the core functionality of SAS Enterprise Miner and shows how to perform basic data mining tasks. Provides step-by-step examples that create a complete process-flow diagram including graphic results.
