  In exploratory tasks involving high-dimensional datasets, dimensionality
reduction (DR) techniques help analysts to discover patterns and other useful
information. Although scatter plot representations of DR results allow for
cluster identification and similarity analysis, such a visual metaphor presents
problems when the number of instances of the dataset increases, resulting in
cluttered visualizations. In this work, we propose a scatter plot-based
multilevel approach to display DR results and address clutter-related problems
when visualizing large datasets, together with the definition of a methodology
to use focus+context interaction on non-hierarchical embeddings. The proposed
technique, called ExplorerTree, uses a sampling selection technique on scatter
plots to reduce visual clutter and guide users through exploratory tasks. We
demonstrate ExplorerTree's effectiveness through a use case, where we visually
explore activation images of the convolutional layers of a neural network.
Finally, we also conducted a user experiment to evaluate ExplorerTree's ability
to convey embedding structures using different sampling strategies.
