Text Mining and Visualization: Case Studies Using Open-Source Tools, Hofmann Markus, Chisholm Andrew

Автор: Hwang, Jaejin (northern Illinois University, United States) Yoon, Youngjin (pitney Bowes, Acadia Ln, Shelton, Ct, United States)
Название: Data analytics and visualization in quality analysis using tableau
ISBN: 0367744147 ISBN-13(EAN): 9780367744144
Издательство: Taylor&Francis
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Цена: 86760.00 T
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Описание: Data Analytics and Visualization in Quality Analysis using Tableau goes beyond the existing quality statistical analysis. It helps quality practitioners perform effective quality control and analysis using Tableau, a user-friendly data analytics and visualization software. It begins with a basic introduction to quality analysis with Tableau including differentiating factors from other platforms. It is followed by a description of features and functions of quality analysis tools followed by step-by-step instructions on how to use Tableau. Further, quality analysis through Tableau based on open source data is explained based on five case studies. Lastly, it systematically describes the implementation of quality analysis through Tableau in an actual workplace via a dashboard example.Features:Describes a step-by-step method of Tableau to effectively apply data visualization techniques in quality analysisFocuses on a visualization approach for practical quality analysisProvides comprehensive coverage of quality analysis topics using state-of-the-art concepts and applications Illustrates pragmatic implementation methodology and instructions applicable to real-world and business casesInclude examples of ready-to-use templates of customizable Tableau dashboardsThis book is aimed at professionals, graduate students and senior undergraduate students in industrial systems and quality engineering, process engineering, systems engineering, quality control, quality assurance and quality analysis.

Автор: Henriette Roued-Cunliffe
Название: Open Heritage Data: Publishing and using open data for visualization, mapping, and mining in cultural heritage institutions
ISBN: 1783303603 ISBN-13(EAN): 9781783303601
Издательство: Facet
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Цена: 190080.00 T
Наличие на складе: Невозможна поставка.
Описание: This book combines current research in open data practices in the heritage sector with technical step-by-step guides on how to work with heritage data for visualisation, mapping, and mining.

Автор: Jones Herbert
Название: Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database
ISBN: 1647483263 ISBN-13(EAN): 9781647483265
Издательство: Неизвестно
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Цена: 27580.00 T
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Описание: Data science has a huge impact on how companies conduct business, and those who don`t learn about this revolutionaryfield could be left behind. You see, data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers.

Автор: Myatt
Название: Making Sense of Data II - A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications
ISBN: 0470222808 ISBN-13(EAN): 9780470222805
Издательство: Wiley
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Цена: 94990.00 T
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Описание: This book provides a general end-to-end discussion concerning the process of translating raw data to scientific and business decisions. The reader`s ability to find patterns in data will be greatly enhanced due to the book`s combination of statistical learning with powerful visualization techniques.

Автор: Zamora Saiz, Alfonso Quesada Gonzalez, Carlos Hurtado Gil, Lluis Mondejar Ruiz, Diego
Название: Introduction to data analysis in r
ISBN: 3030489965 ISBN-13(EAN): 9783030489960
Издательство: Springer
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Цена: 69870.00 T
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Описание: This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R.

Автор: Timothy J. Brown; Paul W. Mielke Jr.
Название: Statistical Mining and Data Visualization in Atmospheric Sciences
ISBN: 1441949747 ISBN-13(EAN): 9781441949745
Издательство: Springer
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Цена: 111790.00 T
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Описание: Statistical Mining and Data Visualization in Atmospheric Sciences brings together in one place important contributions and up-to-date research results in this fast moving area.

Автор: Jin Cheqing, Zhou Aoying, Mao Jiali
Название: Clustering And Outlier Detection For Trajectory Stream Data
ISBN: 9811210454 ISBN-13(EAN): 9789811210457
Издательство: World Scientific Publishing
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Цена: 95040.00 T
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Описание:

As mobile devices continue becoming a larger part of our lives, the development of location acquisition technologies to track moving objects have focused the minds of researchers on issues ranging from longitude and latitude coordinates, speed, direction, and timestamping, as part of parameters needed to calculate the positional information and locations of objects, in terms of time and position in the form of trajectory streams. Recently, recent advances have facilitated various urban applications such as smart transportation and mobile delivery services.

Unlike other books on spatial databases, mobile computing, data mining, or computing with spatial trajectories, this book is focused on smart transportation applications.

This book is a good reference for advanced undergraduates, graduate students, researchers, and system developers working on transportation systems.

Автор: Danai Koutra, Christos Faloutsos
Название: Individual and Collective Graph Mining: Principles, Algorithms, and Applications
ISBN: 1681732475 ISBN-13(EAN): 9781681732473
Издательство: Mare Nostrum (Eurospan)
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Цена: 108110.00 T
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Описание: Presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, the book also contribute graph-theoretical ideas and models, and real-world applications in two main areas - Individual Graph Mining and Collective Graph Mining.

Автор: McGee Fintan, Renoust Benjamin, Archambault Daniel
Название: Visual Analysis of Multilayer Networks
ISBN: 1636391451 ISBN-13(EAN): 9781636391458
Издательство: Mare Nostrum (Eurospan)
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Цена: 89630.00 T
Наличие на складе: Нет в наличии.
Описание: The emergence of multilayer networks as a concept from the field of complex systems provides many new opportunities for the visualization of network complexity, and has also raised many new exciting challenges. The multilayer network model recognizes that the complexity of relationships between entities in real-world systems is better embraced as several interdependent subsystems (or layers) rather than a simple graph approach. Despite only recently being formalized and defined, this model can be applied to problems in the domains of life sciences, sociology, digital humanities, and more. Within the domain of network visualization there already are many existing systems, which visualize data sets having many characteristics of multilayer networks, and many techniques, which are applicable to their visualization. In this Synthesis Lecture, we provide an overview and structured analysis of contemporary multilayer network visualization. This is not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those solving problems within application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer network visualization, as well as tools, tasks, and analytic techniques from within application domains. We also identify the research opportunities and examine outstanding challenges for multilayer network visualization along with potential solutions and future research directions for addressing them.

Автор: Mirman
Название: Growth Curve Analysis and Visualization Using R
ISBN: 1466584327 ISBN-13(EAN): 9781466584327
Издательство: Taylor&Francis
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Цена: 91860.00 T
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Описание: Learn How to Use Growth Curve Analysis with Your Time Course Data An increasingly prominent statistical tool in the behavioral sciences, multilevel regression offers a statistical framework for analyzing longitudinal or time course data. It also provides a way to quantify and analyze individual differences, such as developmental and neuropsychological, in the context of a model of the overall group effects. To harness the practical aspects of this useful tool, behavioral science researchers need a concise, accessible resource that explains how to implement these analysis methods. Growth Curve Analysis and Visualization Using R provides a practical, easy-to-understand guide to carrying out multilevel regression/growth curve analysis (GCA) of time course or longitudinal data in the behavioral sciences, particularly cognitive science, cognitive neuroscience, and psychology. With a minimum of statistical theory and technical jargon, the author focuses on the concrete issue of applying GCA to behavioral science data and individual differences. The book begins with discussing problems encountered when analyzing time course data, how to visualize time course data using the ggplot2 package, and how to format data for GCA and plotting. It then presents a conceptual overview of GCA and the core analysis syntax using the lme4 package and demonstrates how to plot model fits. The book describes how to deal with change over time that is not linear, how to structure random effects, how GCA and regression use categorical predictors, and how to conduct multiple simultaneous comparisons among different levels of a factor. It also compares the advantages and disadvantages of approaches to implementing logistic and quasi-logistic GCA and discusses how to use GCA to analyze individual differences as both fixed and random effects. The final chapter presents the code for all of the key examples along with samples demonstrating how to report GCA results. Throughout the book, R code illustrates how to implement the analyses and generate the graphs. Each chapter ends with exercises to test your understanding. The example datasets, code for solutions to the exercises, and supplemental code and examples are available on the author’s website.

Автор: Abhinav Bhatele; David Boehme; Joshua A. Levine; A
Название: Programming and Performance Visualization Tools
ISBN: 3030178714 ISBN-13(EAN): 9783030178710
Издательство: Springer
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Цена: 54030.00 T
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Описание: This book contains the revised selected papers of 4 workshops held in conjunction with the International Conference on High Performance Computing, Networking, Storage and Analysis (SC) in November 2017 in Denver, CO, USA, and in November 2018 in Dallas, TX, USA: the 6th and 7th International Workshop on Extreme-Scale Programming Tools, ESPT 2017 and ESPT 2018, and the 4th and 5th International Workshop on Visual Performance Analysis, VPA 2017 and VPA 2018.The 11 full papers of ESPT 2017 and ESPT 2018 and the 6 full papers of VPA 2017 and VPA 2018 were carefully reviewed and selected for inclusion in this book. The papers discuss the requirements for exascale-enabled tools as well as new approaches of applying visualization and visual analytic techniques to large-scale applications. Topics of interest include: programming tools; methodologies for performance engineering; tool technologies for extreme-scale challenges (e.g., scalability, resilience, power); tool support for accelerated architectures and large-scale multi-cores; tool infrastructures and environments; evolving/future application requirements for programming tools and technologies; application developer experiences with programming and performance tools; scalable displays of performance data; case studies demonstrating the use of performance visualization in practice; data models to enable scalable visualization; graph representation of unstructured performance data; presentation of high-dimensional data; visual correlations between multiple data sources; human-computer interfaces for exploring performance data; and multi-scale representations of performance data for visual exploration.

Автор: Berman, Jules J.
Название: Data Simplification
ISBN: 0128037814 ISBN-13(EAN): 9780128037812
Издательство: Elsevier Science
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Цена: 49390.00 T
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Описание: Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools. . This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data. Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.