Download or read book Data Mining Techniques Segmentation with SAS Enterprise Miner written by Scientific Books and published by CreateSpace. This book was released on 2015-05-08 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Customer Segmentation and Clustering Using SAS Enterprise Miner Third Edition written by Randall S. Collica and published by SAS Institute. This book was released on 2017-03-23 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Résumé : A working guide that uses real-world data, this step-by-step resource will show you how to segment customers more intelligently and achieve the one-to-one customer relationship that your business needs. --

Download or read book Customer Segmentation and Clustering Using SAS Enterprise Miner Third Edition written by Randall S. Collica and published by SAS Institute. This book was released on 2017-03-23 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book CRM Segmentation and Clustering Using SAS Enterprise Miner written by Randall S. Collica and published by SAS Press. This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the customer is critical to your company's success. In this instructive guide, Randy Collica employs SAS Enterprise Miner and the most commonly available techniques for customer relationship management (CRM). You will learn how to segment customers more intelligently and to achieve, or at least get closer to, the one-to-one customer relationship that today's businesses want. Step-by-step examples and exercises clearly illustrate the concepts of segmentation and clustering in the context of CRM. The book, with a foreword by Michael J. A. Berry, is sectioned into three parts. 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 and clustering with many attributes. 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 software.This straight-forward guide will appeal to anyone who seeks to better understand customers or prospective customers. Additionally, professors and students will find the book well suited for a business data mining analytics course in an MBA program or related course of study. You should understand basic statistics, but no prior knowledge of data mining or SAS Enterprise Miner is required. Included on your bonus CD-ROM are the following: example SAS code, data sets, macros, and Enterprise Miner templates.

Download or read book Data Mining Techniques in CRM written by Konstantinos K. Tsiptsis and published by John Wiley & Sons. This book was released on 2011-08-24 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.

Download or read book Customer Segmentation and Clustering Using SAS Enterprise Miner Second Edition written by Randall S. Collica and published by SAS Press. This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prev. ed. published under title: CRM segmentation and clustering using SAS Enterprise miner.

Download or read book First Steps in Data Mining with SAS Enterprise Miner written by Martha Abell and published by CreateSpace. This book was released on 2014-09-06 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner written by Olivia Parr-Rud and published by SAS Institute. This book was released on 2014-10 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. This beginnner's guide with clear, illustrated, step-by-step instructions 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. --

Download or read book Data Mining with SAS Enterprise Miner Through Examples written by Cesar Lopez and published by CreateSpace. This book was released on 2013-06-26 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Text Mining and Analysis written by Dr. Goutam Chakraborty and published by SAS Institute. This book was released on 2014-11-22 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Segmentation Analytics with SAS Viya written by Randall S. Collica and published by SAS Institute. This book was released on 2021-07-14 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Better understand your customers using segmentation analytics in SAS Viya! Segmentation Analytics with SAS Viya: An Approach to Clustering and Visualization demonstrates the use of clustering and machine learning methods for the purpose of segmenting customer or client data into useful categories for marketing, market research, next best offers by segment, and more. This book highlights the latest and greatest methods available that show the power of SAS Viya while solving typical industry issues. Packed with real-world examples, this book provides readers with practical methods of using SAS Visual Data Mining and Machine Learning (VDMML), SAS Model Studio, SAS Visual Statistics, SAS Visual Analytics, and coding in SAS Studio for segmentation model development and analysis. This book is designed for analysts, data miners, and data scientists who need to use the all in-memory platform of SAS Viya for the purposes of clustering and segmentation. Understanding how customers behave is a primary objective of most organizations, and segmentation is a key analytic method for achieving that objective.

Download or read book Unknown MIR Title written by SAS Publishing and published by . This book was released on 2001-03 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical and Machine Learning Data Mining written by Bruce Ratner and published by CRC Press. This book was released on 2017-07-12 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Download or read book Data Mining Techniques Predictive Models with SAS Enterprise Miner written by Scientific Books and published by CreateSpace. This book was released on 2015-05-08 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book DATA MINING TECHNIQUES WITH SAS ENTERPRISE MINER PREDICTIVE AND CLASSIFICATION MODELS written by Perez C. Perez and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Decision Trees for Analytics Using SAS Enterprise Miner written by Barry De Ville and published by . This book was released on 2019-07-03 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book SAS Enterprise Miner Data Mining Techniques written by Martha Abell and published by CreateSpace. This book was released on 2014-09-05 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.