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Beschreibung
This textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail. The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows,organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified. The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.
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Technische Daten


Erscheinungsdatum
31.08.2021
Sprache
Englisch
EAN
9783030561482
Herausgeber
Springer International Publishing
Sonderedition
Nein
Autor
Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay, Stefan Wrobel
Seitenanzahl
440
Auflage
1
Einbandart
Broschiert
Autorenporträt
Natalia and Gennady Andrienko are lead scientists responsible for visual analytics research at the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS) in Germany since 2007 and part-time professors at City, University of London since 2013.  They co-authored monographs “Exploratory Analysis of Spatial and Temporal Data” (Springer, 2006) and “Visual Analytics of Movement” (Springer, 2013), and more than 100 peer-reviewed journal papers. Their research interests include geovisualization, information visualization with a focus on spatial and temporal data, visual analytics, interactive knowledge discovery and data mining, and data science. Natalia and Gennady Andrienko received test of time award at IEEE VAST 2018, best paper awards at AGILE 2006, IEEE VAST 2011 and 2012, and EuroVis 2015 conferences and EuroVA 2018 and 2019 workshops, honorable mention awards at IEEE VAST 2010 and EuroVis 2017 conferences, VAST challenge awards 2008 and 2014, and best posterawards at AGILE 2007 and 2018, ACM GIS 2011 and IEEE VAST 2016 conferences.
Schlagwörter
Data Science, Data Analytics, Visual Analytics, Data Mining, Machine Learning
Thema-Inhalt
UNF - Data Mining UYQE - Wissensbasierte Systeme, Expertensysteme UYQ - Künstliche Intelligenz UYQP - Mustererkennung
Höhe
235 mm
Breite
15.5 cm

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