Data Mining Using Enterprise Miner Software
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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 : 2007-08-03
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.
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 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.
Data Science And Machine Learning For Non Programmers
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Author : Dothang Truong
language : en
Publisher: CRC Press
Release Date : 2024-02-23
Data Science And Machine Learning For Non Programmers written by Dothang Truong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-23 with Business & Economics categories.
As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.
Data Mining Techniques With Sas Enterprise Miner Sampling Exporatory Analysis And Association Rules
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Author : Scientific Books
language : en
Publisher: CreateSpace
Release Date : 2015-06-22
Data Mining Techniques With Sas Enterprise Miner Sampling Exporatory Analysis And Association Rules 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 2015-06-22 with categories.
SAS Institute implements data mining in Enterprise Miner software, which will be used in this book focused predictive models. 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.
Getting Started With Sas Enterprise Miner 5 3
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Author : SAS Publishing
language : en
Publisher: Sas Inst
Release Date : 2008-06-01
Getting Started With Sas Enterprise Miner 5 3 written by SAS Publishing and has been published by Sas Inst this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-01 with Computers categories.
SAS Enterprise Miner 5.3 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 5.3 provides step-by-step examples that create a complete process flow diagram, including graphic results. This title is also available online and in hardcopy format. 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.
Sas Enterprise Miner Data Mining Techniques
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Author : Martha Abell
language : en
Publisher: CreateSpace
Release Date : 2014-09-05
Sas Enterprise Miner Data Mining Techniques written by Martha Abell and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-05 with categories.
This book presents the most common techniques used in data mining in a simple and easy to understand way through one of the most common software solutions from existing in the market, namely the SAS software. It seeks to clarify the original purpose as related applications traditionally qualified as difficult or opaque methods. It seeks to present applications of data mining without handle high theoretical mathematical developments or complicated algorithms, which is the most common reason for the difficulties in understanding and application of this material. In the text the concepts of data mining in the simplest way possible, so as to be intelligible to readers with diverse backgrounds are described. The chapter begins by describing the techniques presented in accessible language and then how to address them through practical applications.
Getting Started With Sas Enterprise Miner 5 2
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Author : SAS Institute
language : en
Publisher: SAS Institute
Release Date : 2006
Getting Started With Sas Enterprise Miner 5 2 written by SAS Institute 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 with Computers categories.
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.
Data Mining Techniques Predictive Models With Sas Enterprise Miner
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Author : Scientific Books
language : en
Publisher: CreateSpace
Release Date : 2015-05-08
Data Mining Techniques Predictive Models 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 predictive models. 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 ENVIRONMENT MODELLING PREDICTIVE TECHNIQUES WITH SAS ENTERPRISE MINER REGRESSION NODE: MULTIPLE REGRESSION MODEL LOGISTIC REGRESSION DMINE REGRESSION NODE PARTIAL LEAST SQUARES NODE. PLS REGRESSION LARS NODE CLASSIFICATION PREDICTIVE TECHNIQUES. DECISION TREES WITH SAS ENTERPRISE MINER DECISION TREE NODE PREDICTIVE MODELS WITH NEURAL NETWORKS WITH SAS ENTERPRISE MINER OPTIMIZATION AND ADJUSTMENT OF MODELS WITH NETS: NEURAL NETWORK NODE SIMPLE NEURAL NETWORKS PERCEPTRONS HIDDEN LAYERS MULTILAYER PERCEPTRONS (MLPS) RADIAL BASIS FUNCTION (RBF) NETWORKS SCORING AUTONEURAL NODE NETWORK ARCHITECTURES NEURAL NODE TWOSTAGE NODE GRADIENT BOOSTING NODE MEMORY-BASED REASONING (MBR) NODE RULE INDUCTION NODE ENSEMBLE NODE COMBINING MODELS USING THE ENSEMBLE NODE MODEL IMPORT NODE SVM NODE ASSESS PHASE IN DATA MINING PROCESS CUTOFF NODE DECISIONS NODE MODEL COMPARISON NODE SCORE NODE
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.