UnTangle: Visual Analysis of Probabilistic Multi-Label Data

Abstract:
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.

Publications:

  • Nan Cao, Yu-Ru Lin, David Gotz, UnTangle Map: Visual Analysis of Probabilistic Multi-Label Data, IEEE Transactions on Visualization and Computer Graphics, IEEE Transaction on Visualization and Computer Graphics (TVCG) (paper | slides)
  • Yu-Ru Lin, Nan Cao, David Gotz, Lu Lu, UnTangle: Visual Analysis of Probabilistic Multi-Label Data, IEEE International Conference on Data Mining (ICDM 2014) (paper | slides)