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UnTangle Map: Visual analysis of probabilistic multi-label data

Cao, N and Lin, YR and Gotz, D (2016) UnTangle Map: Visual analysis of probabilistic multi-label data. IEEE Transactions on Visualization and Computer Graphics, 22 (2). 1149 - 1163. ISSN 1077-2626

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Data with multiple probabilistic labels are common in many situations. For example, a movie may be associated with multiple genres with different levels of confidence. Despite their ubiquity, the problem of visualizing probabilistic labels has not been adequately addressed. Existing approaches often either discard the probabilistic information, or map the data to a low-dimensional subspace where their associations with original labels are obscured. In this paper, we propose a novel visual technique, UnTangle Map, for visualizing probabilistic multi-labels. In our proposed visualization, data items are placed inside a web of connected triangles, with labels assigned to the triangle vertices such that nearby labels are more relevant to each other. The positions of the data items are determined based on the probabilistic associations between items and labels. UnTangle Map provides both (a) an automatic label placement algorithm, and (b) adaptive interactions that allow users to control the label positioning for different information needs. Our work makes a unique contribution by providing an effective way to investigate the relationship between data items and their probabilistic labels, as well as the relationships among labels. Our user study suggests that the visualization effectively helps users discover emergent patterns and compare the nuances of probabilistic information in the data labels.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Cao, N
Lin, YRYURULIN@pitt.eduYURULIN0000-0002-8497-3015
Gotz, D
Date: 1 February 2016
Date Type: Publication
Journal or Publication Title: IEEE Transactions on Visualization and Computer Graphics
Volume: 22
Number: 2
Page Range: 1149 - 1163
DOI or Unique Handle: 10.1109/tvcg.2015.2424878
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 1077-2626
Date Deposited: 29 Jun 2015 20:07
Last Modified: 31 Mar 2021 08:55


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