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Information visualization
Information visualization or information visualisation is the study of (interactive) visual representations of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text and geographic information. However, information visualization differs from scientific visualization: "it’s infovis (visualization ) when the spatial representation is chosen, and it’s scivis (visualization ) when the spatial representation is given".〔http://www.cs.ubc.ca/labs/imager/tr/2008/pitfalls/〕
== Overview ==
The field of information visualization has emerged "from research in human-computer interaction, computer science, graphics, visual design, psychology, and business methods. It is increasingly applied as a critical component in scientific research, digital libraries, data mining, financial data analysis, market studies, manufacturing production control, and drug discovery".〔Benjamin B. Bederson and Ben Shneiderman (2003). (''The Craft of Information Visualization: Readings and Reflections'' ), Morgan Kaufmann ISBN 1-55860-915-6.〕
Information visualization presumes that "visual representations and interaction techniques take advantage of the human eye’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. Information visualization focused on the creation of approaches for conveying abstract information in intuitive ways."〔James J. Thomas and Kristin A. Cook (Ed.) (2005). (''Illuminating the Path: The R&D Agenda for Visual Analytics'' ). National Visualization and Analytics Center. p.30〕
Data analysis is an indispensable part of all applied research and problem solving in industry. The most fundamental data analysis approaches are visualization (histograms, scatter plots, surface plots, tree maps, parallel coordinate plots, etc.), statistics (hypothesis test, regression, PCA, etc.), data mining (association mining, etc.), and machine learning methods (clustering, classification, decision trees, etc.). Among these approaches, information visualization, or visual data analysis, is the most reliant on the cognitive skills of human analysts, and allows the discovery of unstructured actionable insights that are limited only by human imagination and creativity. The analyst does not have to learn any sophisticated methods to be able to interpret the visualizations of the data. Information visualization is also a hypothesis generation scheme, which can be, and is typically followed by more analytical or formal analysis, such as statistical hypothesis testing.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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