Computer graphics focus in how to display the information to the end users. Because of this data– driven nature, scientific visualization is also named “data visualization.”ĭV includes components from various disciplines, such as user interface (ergonomics), cognitive/perceptual psychology, database management, statistical analysis, and data mining. Usually data visualizers do not know how the graphical representation of the data would turn out. Keim (2002) argued that “for data mining to be effective, it is important to include the human in the data exploration process.” Keim (2002) proposed “visual data mining” Some researchers also call for “graph mining” as an extension of data mining (Samatova, Hendrix, Jenkins, Padmanabhan, & Chakraborty, 2014).Ĭomputer graphic designers and animators devote effort to make electronic imitations of a known object or to actualize a planned draft whereas scientific visualization often deals with abstract data. Many data mining techniques are automated by machine learning. Many EDA and data mining techniques are not entirely based on visual inspection though the results can be graphically presented. All visualization is based on analogy and all analogy is incomplete.ĭV and EDA/data mining are by no means synonymous. These analogies are not in any sense more direct than the analogy based upon transformed data. This view overlooks the fact that raw data produced by various measurement methods such as Likert–scale, IQ test, MPPI, and many others are also human–made representations of an object of study. They prefer plots of raw data to plots of transformed data because the latter is not as “real” and “direct” as the former. For the purpose of exploration the tool should be interactive and dynamic.Ĥ No direct analogy Some researchers still conceptualize graphical information as some kind of “one–to–one–mapping” between the symbols and the world. A common ground shared by EDA and data mining: The data visualizer should explore the data in as many ways as possible until a plausible story of the data emerges. The process of exploring and displaying data in a manner that builds a visual analogy in the service of researcher insight and learning. Why do we need data visualization? A brief history of data visualization 1 Data visualization: Dancing with the dataĢ Agenda What is data visualization? What isn’t data visualization?
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