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Author : Kattamuri S. Sarma
Publisher : SAS Institute
Page : 574 pages
File Size : 16,17 MB
Release : 2017-07-20
Category : Computers
ISBN : 163526040X
« 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 : Randall Matignon
Publisher : AuthorHouse
Page : 608 pages
File Size : 45,69 MB
Release : 2005-08
Category : Computers
ISBN : 1418423416
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.
Author : Randall Matignon
Publisher : John Wiley & Sons
Page : 580 pages
File Size : 24,60 MB
Release : 2007-08-13
Category : Mathematics
ISBN : 0470171421
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 : Dothang Truong
Publisher : CRC Press
Page : 0 pages
File Size : 19,14 MB
Release : 2024-02-23
Category : Business & Economics
ISBN : 9780367755386
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 utilise 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.
Author : Diana Abdul Wahab
Publisher :
Page : pages
File Size : 42,37 MB
Release : 2008
Category :
ISBN :
Author : Scientific Books
Publisher : Createspace Independent Publishing Platform
Page : 252 pages
File Size : 40,63 MB
Release : 2016-01-03
Category :
ISBN : 9781523224685
A neural network can be defined as a set of highly interconnected elements of information processing, which are able to learn the information that feeds them. The main feature of this new technology of neural networks is that it can apply to a large number of problems that can range from complex problems to theoretical sophisticated models such as image recognition, voice recognition, financial analysis, analysis and filtering of signals, etc. In this book we will see applications of neural networks for the improvement of the techniques of classification, discrimination, prediction, etc. Successive chapters present examples that clarify the application of the models in the field of neural networks. The examples are solved step by step with SAS Enterprise Miner in order to make easier the understanding of the methodologies used.
Author : Varol Onur Kayhan
Publisher : Varol Onur Kayhan
Page : 128 pages
File Size : 31,71 MB
Release : 2020-04-07
Category : Education
ISBN :
This book is written for students in higher education. Instructors teaching predictive analytics courses can assign this book to their students to expose them to predictive analytics techniques using SAS Enterprise Miner. The book is developed using SAS Enterprise Miner 14.3, but it should apply to other versions with little to no changes. This book does not require students to have any previous knowledge of SAS Enterprise Miner. It walks students through the predictive analytics process using step-by-step by instructions. Even though the contents of this book can be completed by anyone who has access to SAS Enterprise Miner, knowledge of predictive analytics concepts is essential. Also, this book is not a substitute for any lecture or textbook. It is best if this book is used in parallel to lectures.
Author : Varol Onur Kayhan
Publisher : Varol Onur Kayhan
Page : 122 pages
File Size : 26,98 MB
Release :
Category : Computers
ISBN :
Visit http://sas-book.com to download the data sets used in this workbook. This workbook is written for students in higher education. Instructors teaching predictive analytics courses can assign this workbook to their students to expose them to predictive analytics techniques using SAS Enterprise Miner. The workbook is developed using SAS Enterprise Miner 14.3, but it should apply to other versions with little to no changes. This workbook does not require students to have any previous knowledge of SAS Enterprise Miner. It walks students through the predictive analytics process using step-by-step by instructions. Even though the contents of this workbook can be completed by anyone who has access to SAS Enterprise Miner, knowledge of predictive analytics concepts is essential. Also, this workbook is not a substitute for any lecture or textbook. It is best if this workbook is used in parallel to lectures.
Author : Scientific Books
Publisher : CreateSpace
Page : 332 pages
File Size : 18,7 MB
Release : 2015-05-08
Category :
ISBN : 9781512100037
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
Author : C. Perez
Publisher : Createspace Independent Publishing Platform
Page : 268 pages
File Size : 50,10 MB
Release : 2017-10-17
Category :
ISBN : 9781978373624
The essential aim of this book is to use predictive models for Data Mning. Models of decision trees, regression and neural networks are used to predict various categories. This book shows you how to build decision tree models to predict a categorical target and how to build regression tree models and neural network models to predict a continuous target. Successive chapters present examples that clarify the application of the models in the field of Data Mining. The examples are solved step by step with SAS Enterprise Miner in order to make easier the understanding of the methodologies used. The book begins by introducing the basics of creating a project, manipulating data sources, and navigating through different results windows. Data Miming tools are used to build the main models: Decision Tree, Neural Network, and Regression. These are addressed in considerable detail, with numerous examples of practical business applications that are illustrated with tables, charts, displays, equations, and even manual calculations that let you see the essence of what Enterprise Miner is doing when it estimates or optimizes a given model.