NDLI: Visual quality metrics and human perception: an initial study on 2D projections of large multidimensional data

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Visual quality metrics and human perception: an initial study on 2D projections of large multidimensional data

Content Provider	ACM Digital Library
Author	Tatu, Andrada Keim, Daniel Schneidewind, Joern Bertini, Enrico Bak, Peter
Abstract	Visual quality metrics have been recently devised to automatically extract interesting visual projections out of a large number of available candidates in the exploration of high-dimensional databases. The metrics permit for instance to search within a large set of scatter plots (e.g., in a scatter plot matrix) and select the views that contain the best separation among clusters. The rationale behind these techniques is that automatic selection of "best" views is not only useful but also necessary when the number of potential projections exceeds the limit of human interpretation. While useful as a concept in general, such metrics received so far limited validation in terms of human perception. In this paper we present a perceptual study investigating the relationship between human interpretation of clusters in 2D scatter plots and the measures automatically extracted out of them. Specifically we compare a series of selected metrics and analyze how they predict human detection of clusters. A thorough discussion of results follows with reflections on their impact and directions for future research.
Starting Page	49
Ending Page	56
Page Count	8
File Format	PDF
ISBN	9781450300766
DOI	10.1145/1842993.1843002
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-05-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Visual quality metrics User study
Content Type	Text
Resource Type	Article

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