Ensembles are large, multidimensional, multivariate datasets generated in areas like physical and natural science to study real-world phenomena. Simulations or experiments are run repeatedly with slightly different initial parameters, producing members of the ensemble. The need to compare data and spatial properties, both within an individual member and across multiple members, makes analysis challenging. Initial visualization techniques focused on ensembles with a limited number of members. Others generated overviews of larger ensembles, but at the expense of aggregating potentially important details. We propose an approach that combines these two directions by automatically clustering members in ways that help scientists locate interesting subsets, then visualize members within the subset. Our ensemble visualization technique includes: (1) octree comparison and clustering to generate a hierarchical level-of-detail overview of inter-member shape and data similarity; (2) a glyph-based visualization of an ensemble member; and (3) a method of combining multiple glyph visualizations to highlight similarities and differences in shape and data values across a subset of ensemble members. We apply our approach to a Relativistic Heavy Ion Collider ensemble collected by nuclear physics colleagues at Duke University studying quantum chromo-dynamics. Our system allows the physicists to interactively choose when to explore inter-member relationships, and when to visualize fine-grained details in individual member datasets.