The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.

The dataset contains 384 links (childs) to any of the BSRN datasets. In this study, we explore multivariate weather phenomena in Antarctica. Since March 1981, a meteorological observatory program has been carried out at Neumayer Station (NM) (70°37' S, 8°22' W), located in Antarctica. NM is an integral part of many international networks, organized e.g., by the World Meteorological Organization (WMO). The data helps to close gaps in the global weather and climate observing networks. Our contacted domain expert is Dr. Gert König-Langlo, scientific leader of the meteorological observatory of Neumayer. The provided mixed data set consists of 26 attributes with measurements every three hours for 30 years (92902 time stamps).