Abstract:
Significant effort has been devoted to designing clustering algorithms that
are responsive to user feedback or that incorporate prior domain knowledge in
the form of constraints. However, users desire more expressive forms of
interaction to influence clustering outcomes. In our experiences working with
diverse application scientists, we have identified an interaction style
scatter/gather clustering that helps users iteratively restructure clustering
results to meet their expectations. As the names indicate, scatter and gather
are dual primitives that describe whether clusters in a current segmentation
should be broken up further or, alternatively, brought back together. By
combining scatter and gather operations in a single step, we support very
expressive dynamic restructurings of data. Scatter/gather clustering is
implemented using a nonlinear optimization framework that achieves both
locality of clusters and satisfaction of user-supplied constraints. We
illustrate the use of our scatter/gather clustering approach in a visual
analytic application to study baffle shapes in the bat biosonar (ears and
nose) system. We demonstrate how domain experts are adept at supplying
scatter/gather constraints, and how our framework incorporates these
constraints effectively without requiring numerous instance-level
constraints.