Evaluation of Cluster Identification Performance for Different PCP Variants

Parallel coordinate plots (PCPs) are a well‐known visualization technique for viewing multivariate data. In the past, various visual modifications to PCPs have been proposed to facilitate tasks such as correlation and cluster identification, to reduce visual clutter, and to increase their informatio...

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Vydané v:Computer graphics forum Ročník 29; číslo 3; s. 793 - 802
Hlavní autori: Holten, Danny, Van Wijk, Jarke J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford, UK Blackwell Publishing Ltd 01.06.2010
Predmet:
Ambiguity
Animation
Clusters
Computer graphics
Density
Fuzzy
H.5.2 [Information Interfaces and Presentation]: User Interfaces-Evaluation/Methodology
I.3.3 [Computer Graphics]: Picture/Image Generation-Line and Curve Generation
I.3.3 [Computer Graphics]: Picture/Image Generation-Viewing Algorithms
Identification
Scatter
Studies
User interface
Viewing
Visual
ISSN:0167-7055, 1467-8659
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Shrnutí:Parallel coordinate plots (PCPs) are a well‐known visualization technique for viewing multivariate data. In the past, various visual modifications to PCPs have been proposed to facilitate tasks such as correlation and cluster identification, to reduce visual clutter, and to increase their information throughput. Most modifications pertain to the use of color and opacity, smooth curves, or the use of animation. Although many of these seem valid improvements, only few user studies have been performed to investigate this, especially with respect to cluster identification. We performed a user study to evaluate cluster identification performance – with respect to response time and correctness – of nine PCP variations, including standard PCPs. To generate the variations, we focused on covering existing techniques as well as possible while keeping testing feasible. This was done by adapting and merging techniques, which led to the following novel variations. The first is an effective way of embedding scatter plots into PCPs. The second is a technique for highlighting fuzzy clusters based on neighborhood density. The third is a spline‐based drawing technique to reduce ambiguity. The last is a pair of animation schemes for PCP rotation. We present an overview of the tested PCP variations and the results of our study. The most important result is that a fair number of the seemingly valid improvements, with the exception of scatter plots embedded into PCPs, do not result in significant performance gains.
Bibliografia:ArticleID:CGF1666
istex:061773C6FCDF0882BD457E741D63B73CC8708761
ark:/67375/WNG-CBZMPT6M-9
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ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2009.01666.x