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Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
A Book

by Gary Miner

  • Publisher : Academic Press
  • Release : 2012
  • Pages : 1053
  • ISBN : 012386979X
  • Language : En, Es, Fr & De
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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. -Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible -Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com -Glossary of text mining terms provided in the appendix

Handbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications
A Book

by Robert Nisbet,Gary Miner,Ken Yale

  • Publisher : Elsevier
  • Release : 2017-11-09
  • Pages : 822
  • ISBN : 0124166458
  • Language : En, Es, Fr & De
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Handbook of Statistical Analysis and Data Mining Applications, Second Edition, 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 users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want 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 several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Text Mining and Analysis

Text Mining and Analysis
Practical Methods, Examples, and Case Studies Using SAS

by Dr. Goutam Chakraborty,Murali Pagolu,Satish Garla

  • Publisher : SAS Institute
  • Release : 2014-11-22
  • Pages : 340
  • ISBN : 1612907873
  • Language : En, Es, Fr & De
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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.

Practical Predictive Analytics and Decisioning Systems for Medicine

Practical Predictive Analytics and Decisioning Systems for Medicine
Informatics Accuracy and Cost-Effectiveness for Healthcare Administration and Delivery Including Medical Research

by Linda Miner,Pat Bolding,Joseph Hilbe,Mitchell Goldstein,Thomas Hill,Robert Nisbet,Nephi Walton,Gary Miner

  • Publisher : Academic Press
  • Release : 2014-09-27
  • Pages : 1110
  • ISBN : 012411640X
  • Language : En, Es, Fr & De
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With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost–efficient manner. Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions. Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations Demonstrates methods to help sort through data to make better observations and allow you to make better predictions

Real-World Data Mining

Real-World Data Mining
Applied Business Analytics and Decision Making

by Dursun Delen

  • Publisher : FT Press
  • Release : 2014-12-16
  • Pages : 288
  • ISBN : 0133551113
  • Language : En, Es, Fr & De
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Use the latest data mining best practices to enable timely, actionable, evidence-based decision making throughout your organization! Real-World Data Mining demystifies current best practices, showing how to use data mining to uncover hidden patterns and correlations, and leverage these to improve all aspects of business performance. Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen delivers an optimal balance of concepts, techniques and applications. Without compromising either simplicity or clarity, he provides enough technical depth to help readers truly understand how data mining technologies work. Coverage includes: processes, methods, techniques, tools, and metrics; the role and management of data; text and web mining; sentiment analysis; and Big Data integration. Throughout, Delen's conceptual coverage is complemented with application case studies (examples of both successes and failures), as well as simple, hands-on tutorials. Real-World Data Mining will be valuable to professionals on analytics teams; professionals seeking certification in the field; and undergraduate or graduate students in any analytics program: concentrations, certificate-based, or degree-based.

Practical Text Analytics

Practical Text Analytics
Interpreting Text and Unstructured Data for Business Intelligence

by Steven Struhl

  • Publisher : Kogan Page Publishers
  • Release : 2015-07-03
  • Pages : 272
  • ISBN : 0749474025
  • Language : En, Es, Fr & De
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In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs. Practical Text Analytics is an essential daily reference resource, providing real-world guidance on the effective application of text analytics. The book presents the analysis process so that it is immediately understood by the marketing professionals who must use it, so they can apply proven concepts and methods correctly and with confidence. By decoding industry terminology and demonstrating practical application of data models once reserved for experts, Practical Text Analytics shows marketers how to frame the right questions, identify key themes and find hidden meaning from unstructured data. Readers will learn to develop powerful new marketing strategies to elevate customer experience, solidify brand value and elevate reputation. Online resources include self-test questions, chapter review Q&A and an Instructor's Manual with text sources and instructions.

Text Data Management and Analysis

Text Data Management and Analysis
A Practical Introduction to Information Retrieval and Text Mining

by ChengXiang Zhai,Sean Massung

  • Publisher : Morgan & Claypool
  • Release : 2016-06-30
  • Pages : 530
  • ISBN : 1970001186
  • Language : En, Es, Fr & De
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Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic. This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.

HEALTHCARE's OUT SICK - PREDICTING A CURE - Solutions that WORK !!!!

HEALTHCARE's OUT SICK - PREDICTING A CURE - Solutions that WORK !!!!
Predictive Analytic Modeling, Decision Making, INNOVATIONS and Precision Medicine Necessary to Correct the Broken Healthcare Delivery System

by Gary D. Miner,Linda Miner,Darrell L. Dean

  • Publisher : CRC Press
  • Release : 2019-01-04
  • Pages : 280
  • ISBN : 042901581X
  • Language : En, Es, Fr & De
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The U.S. healthcare system is in "complete chaos-disarray." Medical costs have increased significantly over the past 6 years with 70% increase for deductibles and 24% or more for health insurance premiums. All the while, workers earnings have either not increased or if they did, the pay raises were for less than the increase in the cost of medical care. The situation is unsustainable and the public wants the system fixed. This book offers ways of fixing the problems in healthcare. HEALTHCARE's OUT SICK - PREDICTING A CURE - Solutions that WORK !!!! first defines the "healthcare in crisis" problem. Through real patient experiences, the book describes the difficulties of getting through the maze of complexity among the plethora of "silo providers" which make up the industry. The heart of the book provides readers with a comprehensive solution that can work, a disruption that is necessary to provide Americans the medical care they need without the US public and healthcare providers and payors going into bankruptcy, insolvency or closure. This book delves into digitized medicine, payor and provider reimbursement models, and value-based healthcare delivery. It also includes a philosophy or mode of thinking and operation for the solutions that are needed for diagnosis-effective, cost-effective, and time-efficient healthcare delivery, of which digitized medicine, value-based care, and payor reimbursement modes are just some of the factors. The authors propose that the real solution involves having the patient at the center of the issues and changing from an archaic gold standard way of thinking to a "Predictive Analytic thinking" where one gets at the real truth by doing "real science" that in the end becomes effective not only for the population but for the individual person. This all leads to real person-centered and person-directed medicine and healthcare delivery.

Text Mining and Analysis

Text Mining and Analysis
Practical Methods, Examples, and Case Studies Using SAS

by Goutam Chakraborty,Murali Pagolu,Satish Garla

  • Publisher : SAS Institute
  • Release : 2013-10
  • Pages : 320
  • ISBN : 161290551X
  • Language : En, Es, Fr & De
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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.

Fundamentals of Predictive Text Mining

Fundamentals of Predictive Text Mining
A Book

by Sholom M. Weiss,Nitin Indurkhya,Tong Zhang

  • Publisher : Springer Science & Business Media
  • Release : 2010-06-14
  • Pages : 226
  • ISBN : 9781849962261
  • Language : En, Es, Fr & De
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One consequence of the pervasive use of computers is that most documents originate in digital form. Widespread use of the Internet makes them readily available. Text mining – the process of analyzing unstructured natural-language text – is concerned with how to extract information from these documents. Developed from the authors’ highly successful Springer reference on text mining, Fundamentals of Predictive Text Mining is an introductory textbook and guide to this rapidly evolving field. Integrating topics spanning the varied disciplines of data mining, machine learning, databases, and computational linguistics, this uniquely useful book also provides practical advice for text mining. In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Background on data mining is beneficial, but not essential. Where advanced concepts are discussed that require mathematical maturity for a proper understanding, intuitive explanations are also provided for less advanced readers. Topics and features: presents a comprehensive, practical and easy-to-read introduction to text mining; includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter; explores the application and utility of each method, as well as the optimum techniques for specific scenarios; provides several descriptive case studies that take readers from problem description to systems deployment in the real world; includes access to industrial-strength text-mining software that runs on any computer; describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English); contains links to free downloadable software and other supplementary instruction material. Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students. Dr. Sholom M. Weiss is a Research Staff Member with the IBM Predictive Modeling group, in Yorktown Heights, New York, and Professor Emeritus of Computer Science at Rutgers University. Dr. Nitin Indurkhya is Professor at the School of Computer Science and Engineering, University of New South Wales, Australia, as well as founder and president of data-mining consulting company Data-Miner Pty Ltd. Dr. Tong Zhang is Associate Professor at the Department of Statistics and Biostatistics at Rutgers University, New Jersey.

Text Mining Application Programming

Text Mining Application Programming
A Book

by Manu Konchady

  • Publisher : Unknown Publisher
  • Release : 2006
  • Pages : 412
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Text Mining Application Programming teaches software developers how to mine the vast amounts of information available on the Web, internal networks, and desktop files and turn it into usable data. The book helps developers understand the problems associated with managing unstructured text, and explains how to build your own mining tools using standard statistical methods from information theory, artificial intelligence, and operations research. Each of the topics covered are thoroughly explained and then a practical implementation is provided.The book begins with a brief overview of text data, where it can be found, and the typical search engines and tools used to search and gather this text. It details how to build tools for extracting and using the text, and covers the mathematics behind many of the algorithms used in building these tools. From there you'll learn how to build tokens from text, construct indexes, and detect patterns in text. You'll also find methods to extract the names of people, places, and organizations from an email, a news article, or a Web page. The next portion of the book teaches you how to find information on the Web, the structure of the Web, and how to build spiders to crawl the Web. Text categorization is also described in the context of managing email. The final part of the book covers information monitoring, summarization, and a simple Question & Answer (Q&A) system. The code used in the book is written in Perl, but knowledge of Perl is not necessary to run the software. Developers with an intermediate level of experience with Perl can customize the software. Although the book is about programming, methods are explained with English-like pseudocode and the source code is provided on the CD-ROM. After reading this book, you'll be ready to tap into the bevy of information available online in ways you never thought possible.

Mastering Text Mining with R

Mastering Text Mining with R
A Book

by Ashish Kumar,Avinash Paul

  • Publisher : Packt Publishing Ltd
  • Release : 2016-12-28
  • Pages : 258
  • ISBN : 1782174702
  • Language : En, Es, Fr & De
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Master text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the text mining process with lucid implementation in the R language Example-rich guide that lets you gain high-quality information from text data Who This Book Is For If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful. What You Will Learn Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process Access and manipulate data from different sources such as JSON and HTTP Process text using regular expressions Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA) Build a baseline sentence completing application Perform entity extraction and named entity recognition using R In Detail Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media. Style and approach This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.

Searcher

Searcher
The Magazine for Database Professionals

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2008
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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人文及社會科學集刊

人文及社會科學集刊
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2015-12
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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R Data Mining

R Data Mining
Implement data mining techniques through practical use cases and real world datasets

by Andrea Cirillo

  • Publisher : Packt Publishing Ltd
  • Release : 2017-11-29
  • Pages : 442
  • ISBN : 1787129233
  • Language : En, Es, Fr & De
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Mine valuable insights from your data using popular tools and techniques in R About This Book Understand the basics of data mining and why R is a perfect tool for it. Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it. Apply effective data mining models to perform regression and classification tasks. Who This Book Is For If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required. What You Will Learn Master relevant packages such as dplyr, ggplot2 and so on for data mining Learn how to effectively organize a data mining project through the CRISP-DM methodology Implement data cleaning and validation tasks to get your data ready for data mining activities Execute Exploratory Data Analysis both the numerical and the graphical way Develop simple and multiple regression models along with logistic regression Apply basic ensemble learning techniques to join together results from different data mining models Perform text mining analysis from unstructured pdf files and textual data Produce reports to effectively communicate objectives, methods, and insights of your analyses In Detail R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets. Style and approach This book takes a practical, step-by-step approach to explain the concepts of data mining. Practical use-cases involving real-world datasets are used throughout the book to clearly explain theoretical concepts.

Current Index to Statistics, Applications, Methods and Theory

Current Index to Statistics, Applications, Methods and Theory
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1998
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.

Unstructured Data Analysis

Unstructured Data Analysis
Entity Resolution and Regular Expressions in SAS

by Matthew Windham

  • Publisher : SAS Institute
  • Release : 2018-09-14
  • Pages : 166
  • ISBN : 1635267099
  • Language : En, Es, Fr & De
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Unstructured data is the most voluminous form of data in the world, and several elements are critical for any advanced analytics practitioner leveraging SAS software to effectively address the challenge of deriving value from that data. This book covers the five critical elements of entity extraction, unstructured data, entity resolution, entity network mapping and analysis, and entity management. By following examples of how to apply processing to unstructured data, readers will derive tremendous long-term value from this book as they enhance the value they realize from SAS products.

Library & Information Science Abstracts

Library & Information Science Abstracts
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2008
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Practical Text Mining with Perl

Practical Text Mining with Perl
A Book

by Roger Bilisoly

  • Publisher : John Wiley & Sons Incorporated
  • Release : 2008-08-18
  • Pages : 295
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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"This book is devoted to the fundamentals of text mining using Perl, an open-source programming tool that is freely available via the Internet. It covers mining ideas from several perspectives - statistics, data mining, linguistics, and information retrieval - and provides readers with the means to successfully complete text mining tasks on their own."--BOOK JACKET.

Computers in Libraries

Computers in Libraries
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1998
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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