ExplorerTree: A Focus+Context Exploration Approach for 2D Embeddings

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. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

In this study, a scatter plot-based multilevel approach is proposed to display dimensionality reduction results and address clutter-related problems in visualizing large datasets. The technique uses sampling selection on scatter plots to reduce visual clutter and guide users through exploratory tasks. The effectiveness of the approach is demonstrated through a use case and a user experiment.