Motion capture data is useful for machine learning applications in a variety of domains (e.g. movement improvisation, physical therapy, character animation in games), but many of these domains require large, diverse datasets with data that is difficult to label. This has precipitated the use of unsupervised learning algorithms for analyzing motion capture datasets. However, there is a distinct lack of tools that aid in the qualitative evaluation of these unsupervised algorithms. In this paper, we present the design of MoViz, a novel visualization tool that enables comparative qualitative evaluation of otherwise "black-box" algorithms for pre-processing and clustering large and diverse motion capture datasets. We applied MoViz to the evaluation of three different gesture clustering pipelines used in the LuminAI improvisational dance system. This evaluation revealed features of the pipelines that may not otherwise have been apparent, suggesting directions for iterative design improvements. This use case demonstrates the potential for this tool to be used by researchers and designers in the field of movement and computing seeking to better understand and evaluate the algorithms they are using to make sense of otherwise intractably large and complex datasets.