A Nested Hierarchy of Localized Scatterplots

subplots revealing a local view on subsets of the data to separate the two classes (red/cyan). The HLSP defines a decision tree that can easily be derived even by inexperienced users directly from the visualization. (d) A Scatterplot Matrix (SPLOM) representation is insufficient to reveal any information on how to separate the classes. (e) Imitating the result of the HLSP with traditional brushing (using another tool) is insufficient and misses the clarity of the HLSP. The separating dimension is shown in the enlarged inset on the top right, the green and magenta data items correspond to the middle and most inner subplot in (c), respectively. The trivial plots along the diagonal of the SPLOM have been omitted. Abstract—The simplicity and visual clarity of scatterplots makes them one of the most widely-used visualization techniques for multivariate data. In complex data sets the important information can be hidden in subsets of the data, often obscured in the typical projections of the whole dataset. This paper presents a new interactive method to explore spatially distinct subsets of a dataset within a given projection. Precisely, we introduce a hierarchy of localized scatterplots as a novel visualization technique that allows to create scatterplots within scatterplots. The resulting visualization bears additional information that would otherwise be hidden within the data. To aid the useful interactive creation of such a hierarchy of localized scatterplots by a user we display transitions between scatterplots as animated rotations in 3D. We show the applicability of our visualization and exploration technique for different tasks, including cluster detection, classification, and comparative analyses. Additionally, we introduce a new exploration tool which we call the crossdimensional semantic lens. Our hierarchy of localized scatterplots preserves the visual clarity and simplicity of scatterplots while providing additional and easily interpretable information about local subsets of the data. Keywords-Data visualization; I