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| sas enterprise miner text mining tutorial: Text Mining and Analysis Dr. Goutam Chakraborty, Murali Pagolu, Satish Garla, 2014-11-22 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. |
| sas enterprise miner text mining tutorial: Handbook of Statistical Analysis and Data Mining Applications Robert Nisbet, John Elder, Gary D. Miner, 2009-05-14 The Handbook of Statistical Analysis and Data Mining Applications 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 one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book 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 industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. - Written By Practitioners for Practitioners - Non-technical explanations build understanding without jargon and equations - Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models - Practical advice from successful real-world implementations - Includes extensive case studies, examples, MS PowerPoint slides and datasets - CD-DVD with valuable fully-working 90-day software included: Complete Data Miner - QC-Miner - Text Miner bound with book |
| sas enterprise miner text mining tutorial: TEXT ANALYTICS WITH SAS , 2019 |
| sas enterprise miner text mining tutorial: Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner Olivia Parr-Rud, 2014-10-01 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. |
| sas enterprise miner text mining tutorial: Simulating Data with SAS Rick Wicklin, 2013-04-22 Data simulation is a fundamental technique in statistical programming and research. Rick Wicklin's Simulating Data with SAS brings together the most useful algorithms and the best programming techniques for efficient data simulation in an accessible how-to book for practicing statisticians and statistical programmers. This book discusses in detail how to simulate data from common univariate and multivariate distributions, and how to use simulation to evaluate statistical techniques. It also covers simulating correlated data, data for regression models, spatial data, and data with given moments. It provides tips and techniques for beginning programmers, and offers libraries of functions for advanced practitioners. As the first book devoted to simulating data across a range of statistical applications, Simulating Data with SAS is an essential tool for programmers, analysts, researchers, and students who use SAS software. This book is part of the SAS Press program. |
| sas enterprise miner text mining tutorial: Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications Gary Miner, 2012-01-11 The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities-- |
| sas enterprise miner text mining tutorial: Decision Trees for Business Intelligence and Data Mining Barry De Ville, 2006 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. |
| sas enterprise miner text mining tutorial: Practical Business Analytics Using SAS Shailendra Kadre, Venkat Reddy Konasani, 2015-02-07 Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios. The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations. The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life. Readers will find just enough theory to understand the practical examples and case studies, which cover all industries. Written for a corporate IT and programming audience that wants to upgrade skills or enter the analytics field, this book includes: More than 200 examples and exercises, including code and datasets for practice. Relevant examples for all industries. Case studies that show how to use SAS analytics to identify opportunities, solve complicated problems, and chart a course. Practical Business Analytics Using SAS: A Hands-on Guide gives you the tools you need to gain insight into the data at your fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before. |
| sas enterprise miner text mining tutorial: Exploring SAS Viya Sas Education, 2019-06-14 This first book in the series covers how to access data files, libraries, and existing code in SAS Studio. You also learn about new procedures in SAS Viya, how to write new code, and how to use some of the pre-installed tasks that come with SAS Visual Data Mining and Machine Learning. In the last chapter, you learn how to use the features in SAS Data Preparation to perform data management tasks using SAS Data Explorer, SAS Data Studio, and SAS Lineage Viewer. Also available free as a PDF from sas.com/books. |
| sas enterprise miner text mining tutorial: Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition Randall S. Collica, 2017-03-23 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. -- |
| sas enterprise miner text mining tutorial: Taming Text Grant S. Ingersoll, Thomas S. Morton, Drew Farris, 2013-01-24 Summary Taming Text, winner of the 2013 Jolt Awards for Productivity, is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. This book explores how to automatically organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. The book guides you through examples illustrating each of these topics, as well as the foundations upon which they are built. About this Book There is so much text in our lives, we are practically drowningin it. Fortunately, there are innovative tools and techniquesfor managing unstructured information that can throw thesmart developer a much-needed lifeline. You'll find them in thisbook. Taming Text is a practical, example-driven guide to working withtext in real applications. This book introduces you to useful techniques like full-text search, proper name recognition,clustering, tagging, information extraction, and summarization.You'll explore real use cases as you systematically absorb thefoundations upon which they are built.Written in a clear and concise style, this book avoids jargon, explainingthe subject in terms you can understand without a backgroundin statistics or natural language processing. Examples arein Java, but the concepts can be applied in any language. Written for Java developers, the book requires no prior knowledge of GWT. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. Winner of 2013 Jolt Awards: The Best Books—one of five notable books every serious programmer should read. What's Inside When to use text-taming techniques Important open-source libraries like Solr and Mahout How to build text-processing applications About the Authors Grant Ingersoll is an engineer, speaker, and trainer, a Lucenecommitter, and a cofounder of the Mahout machine-learning project. Thomas Morton is the primary developer of OpenNLP and Maximum Entropy. Drew Farris is a technology consultant, software developer, and contributor to Mahout,Lucene, and Solr. Takes the mystery out of verycomplex processes.—From the Foreword by Liz Liddy, Dean, iSchool, Syracuse University Table of Contents Getting started taming text Foundations of taming text Searching Fuzzy string matching Identifying people, places, and things Clustering text Classification, categorization, and tagging Building an example question answering system Untamed text: exploring the next frontier |
| sas enterprise miner text mining tutorial: Learn R for Applied Statistics Eric Goh Ming Hui, 2018-11-30 Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. What You Will Learn Discover R, statistics, data science, data mining, and big data Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions Work with descriptive statistics Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions Who This Book Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations. |
| sas enterprise miner text mining tutorial: Data Mining and Data Warehousing Parteek Bhatia, 2019-06-27 Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding. |
| sas enterprise miner text mining tutorial: Data Mining for the Masses Matthew North, 2012-08-18 Have you ever found yourself working with a spreadsheet full of data and wishing you could make more sense of the numbers? Have you reviewed sales or operations reports, wondering if there's a better way to anticipate your customers' needs? Perhaps you've even thought to yourself: There's got to be more to these figures than what I'm seeing! Data Mining can help, and you don't need a Ph.D. in Computer Science to do it. You can forecast staffing levels, predict demand for inventory, even sift through millions of lines of customer emails looking for common themes-all using data mining. It's easier than you might think. In Data Mining for the Masses, professor Matt North-a former risk analyst and database developer for eBay.com-uses simple examples, clear explanations and free, powerful, easy-to-use software to teach you the basics of data mining; techniques that can help you answer some of your toughest business questions. You've got data and you know it's got value, if only you can figure out how to unlock it. This book can show you how. Let's start digging! Through an agreement with the Global Text Project, an electronic version of this text is available online at (http://globaltext.terry.uga.edu/books). Proceeds from the sales of printed copies through Amazon enable the author to support the Global Text Project's goal of making electronic texts available to students in developing economies. |
| sas enterprise miner text mining tutorial: SAS Text Analytics for Business Applications Teresa Jade, Biljana Belamaric-Wilsey, Michael Wallis, 2019-03-29 Extract actionable insights from text and unstructured data. Information extraction is the task of automatically extracting structured information from unstructured or semi-structured text. SAS Text Analytics for Business Applications: Concept Rules for Information Extraction Models focuses on this key element of natural language processing (NLP) and provides real-world guidance on the effective application of text analytics. Using scenarios and data based on business cases across many different domains and industries, the book includes many helpful tips and best practices from SAS text analytics experts to ensure fast, valuable insight from your textual data. Written for a broad audience of beginning, intermediate, and advanced users of SAS text analytics products, including SAS Visual Text Analytics, SAS Contextual Analysis, and SAS Enterprise Content Categorization, this book provides a solid technical reference. You will learn the SAS information extraction toolkit, broaden your knowledge of rule-based methods, and answer new business questions. As your practical experience grows, this book will serve as a reference to deepen your expertise. |
| sas enterprise miner text mining tutorial: SAS and R Ken Kleinman, Nicholas J. Horton, 2014-07-17 An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent Tasks The first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications. New to the Second Edition This edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples. Enables Easy Mobility between the Two Systems Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website. |
| sas enterprise miner text mining tutorial: Data Mining Using SAS Enterprise Miner SAS Institute, 2003 |
| sas enterprise miner text mining tutorial: Data Mining with Rattle and R Graham Williams, 2011-02-25 Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings. |
| sas enterprise miner text mining tutorial: Visual Data Mining Simeon Simoff, Michael H. Böhlen, Arturas Mazeika, 2008-07-23 Visual Data Mining—Opening the Black Box Knowledge discovery holds the promise of insight into large, otherwise opaque datasets. Thenatureofwhatmakesaruleinterestingtoauserhasbeendiscussed 1 widely but most agree that it is a subjective quality based on the practical u- fulness of the information. Being subjective, the user needs to provide feedback to the system and, as is the case for all systems, the sooner the feedback is given the quicker it can in?uence the behavior of the system. There have been some impressive research activities over the past few years but the question to be asked is why is visual data mining only now being - vestigated commercially? Certainly, there have been arguments for visual data 2 mining for a number of years – Ankerst and others argued in 2002 that current (autonomous and opaque) analysis techniques are ine?cient, as they fail to - rectly embed the user in dataset exploration and that a better solution involves the user and algorithm being more tightly coupled. Grinstein stated that the “current state of the art data mining tools are automated, but the perfect data mining tool is interactive and highly participatory,” while Han has suggested that the “data selection and viewing of mining results should be fully inter- tive, the mining process should be more interactive than the current state of the 2 art and embedded applications should be fairly automated . ” A good survey on 3 techniques until 2003 was published by de Oliveira and Levkowitz . |
| sas enterprise miner text mining tutorial: Data Preparation for Analytics Using SAS Gerhard Svolba, 2006-11-27 Written for anyone involved in the data preparation process for analytics, Gerhard Svolba's Data Preparation for Analytics Using SAS offers practical advice in the form of SAS coding tips and tricks, and provides the reader with a conceptual background on data structures and considerations from a business point of view. The tasks addressed include viewing analytic data preparation in the context of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations of data preparation for time series analysis, using various SAS procedures and SAS Enterprise Miner for scoring, creating meaningful derived variables for all data mart types, using powerful SAS macros to make changes among the various data mart structures, and more! |
| sas enterprise miner text mining tutorial: Predictive Analytics and Data Mining Vijay Kotu, Bala Deshpande, 2014-11-27 Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.You’ll be able to:1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process.2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases.3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples |
| sas enterprise miner text mining tutorial: SAS For Dummies Stephen McDaniel, Chris Hemedinger, 2010-03-16 The fun and easy way to learn to use this leading business intelligence tool Written by an author team who is directly involved with SAS, this easy-to-follow guide is fully updated for the latest release of SAS and covers just what you need to put this popular software to work in your business. SAS allows any business or enterprise to improve data delivery, analysis, reporting, movement across a company, data mining, forecasting, statistical analysis, and more. SAS For Dummies, 2nd Edition gives you the necessary background on what SAS can do for you and explains how to use the Enterprise Guide. SAS provides statistical and data analysis tools to help you deal with all kinds of data: operational, financial, performance, and more Places special emphasis on Enterprise Guide and other analytical tools, covering all commonly used features Covers all commonly used features and shows you the practical applications you can put to work in your business Explores how to get various types of data into the software and how to work with databases Covers producing reports and Web reporting tools, analytics, macros, and working with your data In the easy-to-follow, no-nonsense For Dummies format, SAS For Dummies gives you the knowledge and the confidence to get SAS working for your organization. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file. |
| sas enterprise miner text mining tutorial: Data Quality for Analytics Using SAS Gerhard Svolba, 2012-04-01 Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba's Data Quality for Analytics Using SAS focuses on selecting the right data sources and ensuring data quantity, relevancy, and completeness. The book is made up of three parts. The first part, which is conceptual, defines data quality and contains text, definitions, explanations, and examples. The second part shows how the data quality status can be profiled and the ways that data quality can be improved with analytical methods. The final part details the consequences of poor data quality for predictive modeling and time series forecasting. With this book you will learn how you can use SAS to perform advanced profiling of data quality status and how SAS can help improve your data quality. This book is part of the SAS Press program. |
| sas enterprise miner text mining tutorial: Hermeneutica Geoffrey Rockwell, Stefan Sinclair, 2022-06-07 An introduction to text analysis using computer-assisted interpretive practices, accompanied by example essays that illustrate the use of these computational tools. The image of the scholar as a solitary thinker dates back at least to Descartes' Discourse on Method. But scholarly practices in the humanities are changing as older forms of communal inquiry are combined with modern research methods enabled by the Internet, accessible computing, data availability, and new media. Hermeneutica introduces text analysis using computer-assisted interpretive practices. It offers theoretical chapters about text analysis, presents a set of analytical tools (called Voyant) that instantiate the theory, and provides example essays that illustrate the use of these tools. Voyant allows users to integrate interpretation into texts by creating hermeneutica—small embeddable “toys” that can be woven into essays published online or into such online writing environments as blogs or wikis. The book's companion website, Hermeneuti.ca, offers the example essays with both text and embedded interactive panels. The panels show results and allow readers to experiment with the toys themselves. The use of these analytical tools results in a hybrid essay: an interpretive work embedded with hermeneutical toys that can be explored for technique. The hermeneutica draw on and develop such common interactive analytics as word clouds and complex data journalism interactives. Embedded in scholarly texts, they create a more engaging argument. Moving between tool and text becomes another thread in a dynamic dialogue. |
| sas enterprise miner text mining tutorial: Intelligent Credit Scoring Naeem Siddiqi, 2017-01-10 A better development and implementation framework for credit risk scorecards Intelligent Credit Scoring presents a business-oriented process for the development and implementation of risk prediction scorecards. The credit scorecard is a powerful tool for measuring the risk of individual borrowers, gauging overall risk exposure and developing analytically driven, risk-adjusted strategies for existing customers. In the past 10 years, hundreds of banks worldwide have brought the process of developing credit scoring models in-house, while ‘credit scores' have become a frequent topic of conversation in many countries where bureau scores are used broadly. In the United States, the ‘FICO' and ‘Vantage' scores continue to be discussed by borrowers hoping to get a better deal from the banks. While knowledge of the statistical processes around building credit scorecards is common, the business context and intelligence that allows you to build better, more robust, and ultimately more intelligent, scorecards is not. As the follow-up to Credit Risk Scorecards, this updated second edition includes new detailed examples, new real-world stories, new diagrams, deeper discussion on topics including WOE curves, the latest trends that expand scorecard functionality and new in-depth analyses in every chapter. Expanded coverage includes new chapters on defining infrastructure for in-house credit scoring, validation, governance, and Big Data. Black box scorecard development by isolated teams has resulted in statistically valid, but operationally unacceptable models at times. This book shows you how various personas in a financial institution can work together to create more intelligent scorecards, to avoid disasters, and facilitate better decision making. Key items discussed include: Following a clear step by step framework for development, implementation, and beyond Lots of real life tips and hints on how to detect and fix data issues How to realise bigger ROI from credit scoring using internal resources Explore new trends and advances to get more out of the scorecard Credit scoring is now a very common tool used by banks, Telcos, and others around the world for loan origination, decisioning, credit limit management, collections management, cross selling, and many other decisions. Intelligent Credit Scoring helps you organise resources, streamline processes, and build more intelligent scorecards that will help achieve better results. |
| sas enterprise miner text mining tutorial: Model Risk Management with SAS , 2020-06-29 Cut through the complexity of model risk management with a guide to solutions from SAS! There is an increasing demand for more model governance and model risk awareness. At the same time, high-performing models are expected to be deployed faster than ever. SAS Model Risk Management is a user-friendly, web-based application that facilitates the capture and life cycle management of statistical model-related information. It enables all stakeholders in the model life cycle - developers, validators, internal audit, and management - to get overview reports as well as detailed information in one central place. Model Risk Management with SAS introduces you to the features and capabilities of this software, including the entry, collection, transfer, storage, tracking, and reporting of models that are drawn from multiple lines of business across an organization. This book teaches key concepts, terminology, and base functionality that are integral to SAS Model Risk Management through hands-on examples and demonstrations. With this guide to SAS Model Risk Management, your organization can be confident it is making fact-based decisions and mitigating model risk. |
| sas enterprise miner text mining tutorial: Data Science and Big Data Analytics EMC Education Services, 2015-01-27 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| sas enterprise miner text mining tutorial: Social Science Research Anol Bhattacherjee, 2012-03-16 This book is designed to introduce doctoral and graduate students to the process of scientific research in the social sciences, business, education, public health, and related disciplines. |
| sas enterprise miner text mining tutorial: Practical Data Mining Jr. Hancock, 2019-09-23 Intended for those who need a practical guide to proven and up-to-date data mining techniques and processes, this book covers specific problem genres. With chapters that focus on application specifics, it allows readers to go to material relevant to their problem domain. Each section starts with a chapter-length roadmap for the given problem domain. This includes a checklist/decision-tree, which allows the reader to customize a data mining solution for their problem space. The roadmap discusses the technical components of solutions. |
| sas enterprise miner text mining tutorial: SAS 9.1.3 Intelligence Platform SAS Institute, 2006 Provides a resource for use if any problems are encountered during an initial installation. |
| sas enterprise miner text mining tutorial: Data Mining Cookbook Olivia Parr Rud, 2001-01 Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining questions. The author, who has over ten years of data mining experience, also provides actual tested models of specific data mining questions for marketing, sales, customer service and retention, and risk management. A CD-ROM, sold separately, provides these models for reader use. |
| sas enterprise miner text mining tutorial: Real World Health Care Data Analysis Douglas Faries, Xiang Zhang, Zbigniew Kadziola, Uwe Siebert, Felicitas Kuehne, Robert L. Obenchain, Josep Maria Haro, 2020 Real world health care data from observational studies, pragmatic trials, patient registries, and databases is common and growing in use. Real World Health Care Data Analysis: Causal Methods and Implementation in SAS® brings together best practices for causal-based comparative effectiveness analyses based on real world data in a single location. Example SAS code is provided to make the analyses relatively easy and efficient.The book also presents several emerging topics of interest, including algorithms for personalized medicine, methods that address the complexities of time varying confounding, extensions of propensity scoring to comparisons between more than two interventions, sensitivity analyses for unmeasured confounding, and implementation of model averaging. |
| sas enterprise miner text mining tutorial: Base SAS 9.3 Procedures Guide, Second Edition , 2012 |
| sas enterprise miner text mining tutorial: Handbook of Statistical Analysis Robert Nisbet, Gary D. Miner, Keith McCormick, 2024-09-16 Handbook of Statistical Analysis: AI and ML Applications, third edition, is a comprehensive introduction to all stages of data analysis, data preparation, model building, and model evaluation. This valuable resource is useful to students and professionals across a variety of fields and settings: business analysts, scientists, engineers, and researchers in academia and industry. General descriptions of algorithms together with case studies help readers understand technical and business problems, weigh the strengths and weaknesses of modern data analysis algorithms, and employ the right analytical methods for practical application. This resource is an ideal guide for users who want to address massive and complex datasets with many standard analytical approaches and be able to evaluate analyses and solutions objectively. It includes clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques; offers accessible tutorials; and discusses their application to real-world problems. - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data analytics to build successful predictive analytic solutions - Provides in-depth descriptions and directions for performing many data preparation operations necessary to generate data sets in the proper form and format for submission to modeling algorithms - Features clear, intuitive explanations of standard analytical tools and techniques and their practical applications - Provides a number of case studies to guide practitioners in the design of analytical applications to solve real-world problems in their data domain - Offers valuable tutorials on the book webpage with step-by-step instructions on how to use suggested tools to build models - Provides predictive insights into the rapidly expanding Intelligence Age as it takes over from the Information Age, enabling readers to easily transition the book's content into the tools of the future |
| sas enterprise miner text mining tutorial: Data Mining Mehmed Kantardzic, 2011-08-16 This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades. If you are an instructor or professor and would like to obtain instructor’s materials, please visit http://booksupport.wiley.com If you are an instructor or professor and would like to obtain a solutions manual, please send an email to: pressbooks@ieee.org |
| sas enterprise miner text mining tutorial: Introduction to Data Mining Using SAS Enterprise Miner Patricia B. Cerrito, 2006 This manual provides a general, practical introduction to data mining using SAS Enterprise Miner and SAS Text Miner software--Preface. |
| sas enterprise miner text mining tutorial: Data Mining Graham J. Williams, 2006-02-20 This volume provides a snapshot of the current state of the art in data mining, presenting it both in terms of technical developments and industrial applications. The collection of chapters is based on works presented at the Australasian Data Mining conferences and industrial forums. Authors include some of Australia's leading researchers and practitioners in data mining. The volume also contains chapters by regional and international authors. |
| sas enterprise miner text mining tutorial: Text Mining and Analysis Dr. Goutam Chakraborty, Murali Pagolu, Satish Garla, 2014-11-22 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. |
| sas enterprise miner text mining tutorial: Research in Personnel and Human Resources Management M. Ronald Buckley, Anthony R. Wheeler, John E. Baur, Jonathon R. B. Halbesleben, 2020-07-24 Research in Personnel and Human Resources Management is designed to promote theory and research on important substantive and methodological topics in the field of human resources management. |
| sas enterprise miner text mining tutorial: SAS Enterprise Miner ile Metin Madenciliği H. Burcu Eskici, N. Alpay Koçak, Bilgisayar teknolojisinde günümüzün sihirli terimi BIG DATA. Büyük veri yığınlarından anlamlı bilgiler, ilişkiler ve istatistikler çıkartmanın en zor yolu, verilerin dağınık düzensiz oluşu. Sayısal verileri düzene sokmak çok daha kolayken verilerin metin olması işleri kat kat zorlaştıran bir unsur. Teknolojinin de gelişmesiyle birlikte kullanılan yapısal olmayan veri miktarı giderek artıyor. Yapısal olmayan veriden bilgi keşfi olarak da tanımlanan “metin madenciliği” yöntemleri veri analizinde doğal dil işleme teknikleri sayesinde başarılı sonuçlar veriyor. Veri kaynaklarında bulunan ve genelde göz ardı edilen yapısal olmayan verinin analizi metin madenciliği yöntemleri ile hızlı ve etkin bir biçimde yapılabiliyor. Metin madenciliği süreci; dokümanların toplanması ve aynı formatta tutularak standartlaştırılması, metnin önişleme sürecinden geçirilerek yapısal veriye dönüştürülmesi olarak özetlenebilir. Tabii bu aşamada doğal dil işleme tekniklerinin devreye girmesi kaçınılmaz. Çünkü metinlerin, cümlelerin, kelimelerin hangi anlama geldiğini, bağlama göre nasıl değiştiğini belirlemek için ileri düzey doğal dil işleme teknikleri kullanılıyor. SAS Enterprise Miner ile Veri Madenciliğine Giriş, bütün bu konuları herkesin anlayabileceği bir dilde örneklerle ele alıyor. Daha da önemlisi, Enterprise Miner yazılımının Türkçe dil desteği sayesinde bütün uygulamaların ve işlemlerin Türkçe üzerinde örneklendirilebilmesi. • Metin Madenciliği • Türkçe Dilbilimsel Analiz• Metin Madenciliği ile Önişleme Teknikleri • Metin Madenciliği Yöntemleri • Metin Sınıflama • Metin Kümeleme• Bilgi Çıkarımı • SAS Enterprise Miner ile Metin Madencilği• İşlemciler• Uygulamada Kullanılan Modeller • Modelleme Sonuçları ve Karşılaştırılması • NACE Faaliyet Sınıflaması Üzerine Bir Uygulama |
如何零基础自学SAS? - 知乎
SAS在四大统计软件中的地位，emmmmm我想想，据说世界500强超过350家在用sas，服不服？ 不管你服不服，反正我是服了 那么对于软件学习，我个人觉得还是系统的上课或者有一本教 …
选硬盘时，该选择SSD/SATA/SAS哪个好？ - 知乎
机械硬盘主要为sata和sas接口，目前家用类别的移动硬盘多为sata接口，sas接口则为企业级应用。 SAS可满足高性能、高可靠性的应用，SATA则满足大容量、非关键业务的应用。
为什么JMP统计软件在中国受众如此之少？ - 知乎
最近在一篇推文中得知sas旗下还有一款软件叫jmp，下载后感觉自己发现新大陆，制图非常方便美观，统计分析板块也基本齐全，但是在国内却鲜有人知，这是为什…
正态性检验的结果怎么看？ - 知乎
在SAS中Kolmogorov-Smirnov一般适用于样本量大于2000，Shapiro-Wilk用于2000以内的样本。 在SPSS中比较复杂，一般而言， 样本量<50 用夏皮洛-威尔克检验（Shapiro-Wilk, W检 …
如何查看自己的笔记本是哪种固态硬盘接口？ - 知乎
机械硬盘5种接口：ide、sata、scsi、sas、fc，比较常见的还是sata！ 固态硬盘的接口有sata、sata express、msata、pci-e、m.2、u.2，其中m.2是兼容pci-e和sata的。
为何3.5寸的机械硬盘需要额外的供电而2.5寸的就不需要？ - 知乎
我今天亲自试过之后的答案，12v1a的机顶盒电源足以带起大部分3.5寸硬盘，硬盘上的标注因该没什么问题，你们可以看看身边或者网图，一般启动电流不会超过标注的12v1a，也就是12w， …
Origin of the phrase, "There's more than one way to skin a cat."
Jun 29, 2011 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for …
word choice - Grandma and Nan, origins and differences? - English ...
Oct 4, 2012 · Etymology. The word nan for grandma is a shortening of the word nana.Both of these words probably are child pronunciations of the word nanny.
To trust someone as far as you can throw them
Apr 18, 2017 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for …
A Question About Quantifier Shift for "each of you" to "you each"
Dec 4, 2015 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for …
如何零基础自学SAS? - 知乎
SAS在四大统计软件中的地位，emmmmm我想想，据说世界500强超过350家在用sas，服不服？ 不管你服不服，反正我是服了 那么对于软件学习，我个人觉得还是系统的上课或者有一本教材为好，对 …
选硬盘时，该选择SSD/SATA/SAS哪个好？ - 知乎
机械硬盘主要为sata和sas接口，目前家用类别的移动硬盘多为sata接口，sas接口则为企业级应用。 SAS可满足高性能、高可靠性的应用，SATA则满足大容量、非关键业务的应用。
为什么JMP统计软件在中国受众如此之少？ - 知乎
最近在一篇推文中得知sas旗下还有一款软件叫jmp，下载后感觉自己发现新大陆，制图非常方便美观，统计分析板块也基本齐全，但是在国内却鲜有人知，这是为什…
正态性检验的结果怎么看？ - 知乎
在SAS中Kolmogorov-Smirnov一般适用于样本量大于2000，Shapiro-Wilk用于2000以内的样本。 在SPSS中比较复杂，一般而言， 样本量<50 用夏皮洛-威尔克检验（Shapiro-Wilk, W检验）； 样本 …
如何查看自己的笔记本是哪种固态硬盘接口？ - 知乎
机械硬盘5种接口：ide、sata、scsi、sas、fc，比较常见的还是sata！ 固态硬盘的接口有sata、sata express、msata、pci-e、m.2、u.2，其中m.2是兼容pci-e和sata的。
为何3.5寸的机械硬盘需要额外的供电而2.5寸的就不需要？ - 知乎
我今天亲自试过之后的答案，12v1a的机顶盒电源足以带起大部分3.5寸硬盘，硬盘上的标注因该没什么问题，你们可以看看身边或者网图，一般启动电流不会超过标注的12v1a，也就是12w，实际上还要 …
Origin of the phrase, "There's more than one way to skin a cat."
Jun 29, 2011 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, …
word choice - Grandma and Nan, origins and differences? - English ...
Oct 4, 2012 · Etymology. The word nan for grandma is a shortening of the word nana.Both of these words probably are child pronunciations of the word nanny.
To trust someone as far as you can throw them
Apr 18, 2017 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, …
A Question About Quantifier Shift for "each of you" to "you each"
Dec 4, 2015 · Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, …
