  Heterogeneous data pose serious challenges to data analysis tasks, including
exploration and visualization. Current techniques often utilize dimensionality
reductions, aggregation, or conversion to numerical values to analyze
heterogeneous data. However, the effectiveness of such techniques to find
subtle structures such as the presence of multiple modes or detection of
outliers is hindered by the challenge to find the proper subspaces or prior
knowledge to reveal the structures. In this paper, we propose a generic
similarity-based exploration technique that is applicable to a wide variety of
datatypes and their combinations, including heterogeneous ensembles. The
proposed concept of similarity has a close connection to statistical analysis
and can be deployed for summarization, revealing fine structures such as the
presence of multiple modes, and detection of anomalies or outliers. We then
propose a visual encoding framework that enables the exploration of a
heterogeneous dataset in different levels of detail and provides insightful
information about both global and local structures. We demonstrate the utility
of the proposed technique using various real datasets, including ensemble data.

Graphics;Human-Computer Interaction