Data Mining With Sas Enterprise Miner Predictive Techniques
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Data Mining With Sas Enterprise Miner Predictive Techniques
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Author : C. Perez
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-10-17
Data Mining With Sas Enterprise Miner Predictive Techniques written by C. Perez and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-17 with categories.
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.
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 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.
Data Mining Techniques With Sas Enterprise Miner Predictive And Classification Models
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Author : Perez C. Perez
language : en
Publisher:
Release Date : 2020
Data Mining Techniques With Sas Enterprise Miner Predictive And Classification Models written by Perez C. Perez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
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
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.
Predictive Modeling With Sas Enterprise Miner
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Author : Kattamuri S. Sarma
language : en
Publisher: SAS Institute
Release Date : 2017-07-20
Predictive Modeling With Sas Enterprise Miner written by Kattamuri S. Sarma and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-20 with Computers categories.
« 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. »--
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.
First Steps In Data Mining With Sas Enterprise Miner
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Author : Martha Abell
language : en
Publisher: CreateSpace
Release Date : 2014-09-06
First Steps In Data Mining With Sas Enterprise Miner 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 2014-09-06 with categories.
SAS Enterprise Miner streamlines the data mining process to create highly accurate predictive and descriptive models based on analysis of vast amounts of data from across an enterprise. Data mining is applicable in a variety of industries and provides methodologies for such diverse business problems as fraud detection, householding, customer retention and attrition, database marketing, market segmentation, risk analysis, affinity analysis, customer satisfaction, bankruptcy prediction, and portfolio analysis. In SAS Enterprise Miner, the data mining process has the following (SEMMA) steps: Sample the data by creating one or more data sets. The sample should be large enough to contain significant information, yet small enough to process. This step includes the use of data preparation tools for data import, merge, append, and filter, as well as statistical sampling techniques. Explore the data by searching for relationships, trends, and anomalies in order to gain understanding and ideas. This step includes the use of tools for statistical reporting and graphical exploration, variable selection methods, and variable clustering. Modify the data by creating, selecting, and transforming the variables to focus the model selection process. This step includes the use of tools for defining transformations, missing value handling, value recoding, and interactive binning. Model the data by using the analytical tools to train a statistical or machine learning model to reliably predict a desired outcome. This step includes the use of techniques such as linear and logistic regression, decision trees, neural networks, partial least squares, LARS and LASSO, nearest neighbor, and importing models defined by other users or even outside SAS Enterprise Miner. Assess the data by evaluating the usefulness and reliability of the findings from the data mining process. This step includes the use of tools for comparing models and computing new fit statistics, cutoff analysis, decision support, report generation, and score code management. You might or might not include all of the SEMMA steps in an analysis, and it might be necessary to repeat one or more of the steps several times before you are satisfied with the results. After you have completed the SEMMA steps, you can apply a scoring formula from one or more champion models to new data that might or might not contain the target variable. Scoring new data that is not available at the time of model training is the goal of most data mining problems. Furthermore, advanced visualization tools enable you to quickly and easily examine large amounts of data in multidimensional histograms and to graphically compare modeling results.
Predictive Modeling With Sas Enterprise Miner
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Author : Kattamuri S. Sarma
language : en
Publisher:
Release Date : 2013-12
Predictive Modeling With Sas Enterprise Miner written by Kattamuri S. Sarma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12 with Business categories.
Learn the theory behind and methods for predictive modeling using SAS Enterprise Miner. Learn how to produce predictive models and prepare presentation-quality graphics in record time with Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition. If you are a graduate student, researcher, or statistician interested in predictive modeling; a data mining expert who wants to learn SAS Enterprise Miner; or a business analyst looking for an introduction to predictive modeling using SAS Enterprise Miner, you'll be able to develop predictive models quickly and effectively using the theory and examples presented in this book. Author Kattamuri Sarma offers the theory behind, programming steps for, and examples of predictive modeling with SAS Enterprise Miner, along with exercises at the end of each chapter. You'll gain a comprehensive awareness of how to find solutions for your business needs. This second edition features expanded coverage of the SAS Enterprise Miner nodes, now including File Import, Time Series, Variable Clustering, Cluster, Interactive Binning, Principal Components, AutoNeural, DMNeural, Dmine Regression, Gradient Boosting, Ensemble, and Text Mining. Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis. Do it all with Predictive Modeling with SAS Enterprise Miner. This book is part of the SAS Press program.