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Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations
Christopher Collins, Gerald Penn, and Sheelagh Carpendale
Abstract
While many data sets contain multiple relationships, depicting more than one data relationship within a single visualization
is challenging. We introduce Bubble Sets as a visualization technique for data that has both a primary data relation with a
semantically significant spatial organization and a significant set membership relation in which members of the same set are not
necessarily adjacent in the primary layout. In order to maintain the spatial rights of the primary data relation, we avoid layout adjustment
techniques that improve set cluster continuity and density. Instead, we use a continuous, possibly concave, isocontour to
delineate set membership, without disrupting the primary layout. Optimizations minimize cluster overlap and provide for calculation
of the isocontours at interactive speeds. Case studies show how this technique can be used to indicate multiple sets on a variety of
common visualizations. |