  Information theory can be used to analyze the cost-benefit of visualization
processes. However, the current measure of benefit contains an unbounded term
that is neither easy to estimate nor intuitive to interpret. In this work, we
propose to revise the existing cost-benefit measure by replacing the unbounded
term with a bounded one. We examine a number of bounded measures that include
the Jenson-Shannon divergence and a new divergence measure formulated as part
of this work. We describe the rationale for proposing a new divergence measure.
As the first part of comparative evaluation, we use visual analysis to support
the multi-criteria comparison, narrowing the search down to several options
with better mathematical properties. The theoretical discourse and conceptual
evaluation in this paper provide the basis for further comparative evaluation
through synthetic and experimental case studies, which are to be reported in a
separate paper.
