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CorrelatedMultiples: Spatially Coherent Small Multiples With Constrained Multi‐Dimensional Scaling
Author(s) -
Liu Xiaotong,
Hu Yifan,
North Stephen,
Shen HanWei
Publication year - 2018
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12526
Subject(s) - multiple , computer science , scaling , visualization , set (abstract data type) , multidimensional scaling , solver , data set , algorithm , forcing (mathematics) , artificial intelligence , mathematics , machine learning , geometry , arithmetic , programming language , mathematical analysis
Displaying small multiples is a popular method for visually summarizing and comparing multiple facets of a complex data set. If the correlations between the data are not considered when displaying the multiples, searching and comparing specific items become more difficult since a sequential scan of the display is often required. To address this issue, we introduce CorrelatedMultiples, a spatially coherent visualization based on small multiples, where the items are placed so that the distances reflect their dissimilarities. We propose a constrained multi‐dimensional scaling (CMDS) solver that preserves spatial proximity while forcing the items to remain within a fixed region. We evaluate the effectiveness of our approach by comparing CMDS with other competing methods through a controlled user study and a quantitative study, and demonstrate the usefulness of CorrelatedMultiples for visual search and comparison in three real‐world case studies .