Documents in rich text corpora often contain multiple facets of information. For example, an article from a medical document collection might consist of multifaceted information about symptoms, treatments, causes, diagnoses, prognoses, and preventions. Thus, documents in the collection may have different relations across each of these various facets. Topic analysis and exploration for such multi-relational corpora is a challenging visual analytic task. To address this challenge, we developed SolarMap, a multifaceted visual analytic technique for visually exploring topics in multi-relational data. SolarMap simultaneously visualizes the topic distribution of the underlying entities from one facet together with keyword distributions that convey the semantic definition of each cluster along a secondary facet. SolarMap combines several visual techniques including 1) topic contour clusters and interactive multifaceted keyword topic rings, 2) a global layout optimization algorithm that aligns each topic cluster with its corresponding keywords, and 3) an optimal temporal network segmentation and layout method that renders temporal evolution of clusters.
David Gotz, Jimeng Sun, Nan Cao. Multifaceted Visual Analytics for Healthcare Applications. To Appear in IBM Journal of Research and Development (Volume 56, Number 5, 2012).
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Nan Cao, David Gotz, Jimeng Sun, Yu-Ru Lin, and Huamin Qu. SolarMap: Multifaceted Visual Analytics for Topic Exploration. IEEE International Conference on Data Mining (ICDM), Vancouver, Canada (2011).
Nan Cao, David Gotz, Jimeng Sun, Yu-Ru Lin, and Huamin Qu. ChronAtlas: A Visualization for Dynamic Topic Exploration. IEEE Information Visualization (InfoVis) Posters, Providence, Rhode Island (2011).