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Toward improved aesthetics and data discrimination for treemaps via color schemes
Author(s) -
Xie Yingtao,
Lin Tao,
Chen Rui,
Chen Zhi
Publication year - 2018
Publication title -
color research and application
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.393
H-Index - 62
eISSN - 1520-6378
pISSN - 0361-2317
DOI - 10.1002/col.22196
Subject(s) - expansive , computer science , value (mathematics) , uncorrelated , degree (music) , artificial intelligence , pattern recognition (psychology) , mathematics , machine learning , statistics , materials science , compressive strength , physics , acoustics , composite material
In this article, we present a novel treemap coloring method which can help users to analyze visual data more easily. Our method overcomes two major limitations of existing treemaps in that they are either aesthetically unpleasing or unable to readily discriminate data blocks with close sizes. Our study indicates that the use of proper color schemes can surprisingly address these two seemingly uncorrelated limitations simultaneously. To improve the aesthetic value of a treemap, we apply the color aesthetic model to treemap generation. To better the degree of data discrimination of similar data, based on the principle of expansive and contractive colors, we propose a novel quantitative color‐visually perceived area (C‐VPA) model via experimental methods. Furthermore, we combine these two models to derive a genetic algorithm‐based treemap coloring method. Our experimental results confirm the superiority of our method in terms of improved data discrimination and aesthetics of the treemaps.