practical text mining and statistical analysis for non structured text data applications

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Practical Text Mining And Statistical Analysis For Non Structured Text Data Applications

Author : Gary Miner
ISBN : 9780123869791
Genre : Mathematics
File Size : 83. 60 MB
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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

Author : Robert Nisbet
ISBN : 9780124166455
Genre : Mathematics
File Size : 86. 50 MB
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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

8th International Conference On Practical Applications Of Computational Biology Bioinformatics Pacbb 2014

Author : Julio Saez-Rodriguez
ISBN : 9783319075815
Genre : Computers
File Size : 72. 50 MB
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Biological and biomedical research are increasingly driven by experimental techniques that challenge our ability to analyse, process and extract meaningful knowledge from the underlying data. The impressive capabilities of next generation sequencing technologies, together with novel and ever evolving distinct types of omics data technologies, have put an increasingly complex set of challenges for the growing fields of Bioinformatics and Computational Biology. The analysis of the datasets produced and their integration call for new algorithms and approaches from fields such as Databases, Statistics, Data Mining, Machine Learning, Optimization, Computer Science and Artificial Intelligence. Clearly, Biology is more and more a science of information requiring tools from the computational sciences. In the last few years, we have seen the surge of a new generation of interdisciplinary scientists that have a strong background in the biological and computational sciences. In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance boosting the research efforts in the field and contributing to the education of a new generation of Bioinformatics scientists. PACBB‘14 contributes to this effort promoting this fruitful interaction. PACBB'14 technical program included 34 papers spanning many different sub-fields in Bioinformatics and Computational Biology. Therefore, the conference promotes the interaction of scientists from diverse research groups and with a distinct background such as computer scientists, mathematicians or biologists.

Computational Intelligence Applications In Business Intelligence And Big Data Analytics

Author : Vijayan Sugumaran
ISBN : 9781351720243
Genre : Computers
File Size : 89. 72 MB
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There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.

Contemporary Research Methods And Data Analytics In The News Industry

Author : Gibbs, William J.
ISBN : 9781466685819
Genre : Language Arts & Disciplines
File Size : 38. 31 MB
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The advent of digital technologies has changed the news and publishing industries drastically. While shrinking newsrooms may be a concern for many, journalists and publishing professionals are working to reorient their skills and capabilities to employ technology for the purpose of better understanding and engaging with their audiences. Contemporary Research Methods and Data Analytics in the News Industry highlights the research behind the innovations and emerging practices being implemented within the journalism industry. This crucial, industry-shattering publication focuses on key topics in social media and video streaming as a new form of media communication as well the application of big data and data analytics for collecting information and drawing conclusions about the current and future state of print and digital news. Due to significant insight surrounding the latest applications and technologies affecting the news industry, this publication is a must-have resource for journalists, analysts, news media professionals, social media strategists, researchers, television news producers, and upper-level students in journalism and media studies. This timely industry resource includes key topics on the changing scope of the news and publishing industries including, but not limited to, big data, broadcast journalism, computational journalism, computer-mediated communication, data scraping, digital media, news media, social media, text mining, and user experience.

Text Mining And Analysis

Author : Dr. Goutam Chakraborty
ISBN : 9781612907871
Genre : Mathematics
File Size : 20. 17 MB
Format : PDF, ePub
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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.

Real World Data Mining

Author : Dursun Delen
ISBN : 9780133551112
Genre : Computers
File Size : 87. 52 MB
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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 Predictive Analytics And Decisioning Systems For Medicine

Author : Linda Miner
ISBN : 9780124116405
Genre : Computers
File Size : 23. 15 MB
Format : PDF, Docs
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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

Trends In Computer Aided Innovation

Author : Noel León-Rovira
ISBN : 9780387754550
Genre : Technology & Engineering
File Size : 42. 42 MB
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Computer Aided Innovation (CAI) is a young domain, the goal of which is to support enterprises throughout the complete innovation process. This comprehensive book presents the most up-to-date research on CAI. It addresses the main motivations of the industrial sector regarding the engineering innovation activity with computer tools and methods. The book also discusses organizational, technological and cognitive aspects of the application of CAI methods and tools.

Text Mining

Author : Sholom M. Weiss
ISBN : 0387345558
Genre : Computers
File Size : 79. 57 MB
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Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.

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