Data Mining Techniques With Sas Enterprise Miner Sampling Exporatory Analysis And Association Rules
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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.
Regression Models And Decision Trees With Sas Enterprise Miner
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Author : Scientific Books
language : en
Publisher: CreateSpace
Release Date : 2015-06-22
Regression Models And Decision Trees 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 2015-06-22 with categories.
SAS Institute implements data mining in Enterprise Miner software, which will be used in this book focused in Sampling Tecniques, Exploratory Analysis and Association Rules. 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.
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.
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.
Data Mining Healthcare And Clinical Databases
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Author : Patricia Cerrito
language : en
Publisher: Lulu.com
Release Date : 2010
Data Mining Healthcare And Clinical Databases written by Patricia Cerrito and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
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.
Unknown Mir Title
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Author : SAS Publishing
language : en
Publisher:
Release Date : 2001-03
Unknown Mir Title written by SAS Publishing and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-03 with categories.
Data Mining Using Enterprise Miner Software
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Author : SAS Institute
language : en
Publisher: SAS Press
Release Date : 2000-01-01
Data Mining Using Enterprise Miner Software written by SAS Institute and has been published by SAS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01-01 with Computers categories.
This introductory guide uses a case study approach to take you through the Enterprise Miner interface from initial data access to a completed association analysis. If you are a new Enterprise Miner user, you will find this guide to be an invaluable resource as you navigate the interface. After completing the case study in this guide, you will be prepared to tackle the more complicated statistical analyses that are covered in the Enterprise Miner online reference documentation. This title is available for purchase as a hardcopy book.
Data Science And Machine Learning For Non Programmers
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Author : Dothang Truong
language : en
Publisher: CRC Press
Release Date : 2024-02-23
Data Science And Machine Learning For Non Programmers written by Dothang Truong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-23 with Business & Economics categories.
As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.
Text Mining And Analysis
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Author : Dr. Goutam Chakraborty
language : en
Publisher: SAS Institute
Release Date : 2014-11-22
Text Mining And Analysis written by Dr. Goutam Chakraborty and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-22 with Computers categories.
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.