# ExplorerTree: a focus+context exploration approach for 2D embeddings

arXiv: https://arxiv.org/abs/2106.10592 · HTML: https://mlsanity.com/paper/2106.10592/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2021.

## Built on by (most cited first, 3 of 3)

- [DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps](https://mlsanity.com/paper/2205.06935/) (2022)
- [VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees](https://mlsanity.com/paper/2112.00334/) (2021)
- [DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps](https://mlsanity.com/paper/2304.00133/) (2023)

## Abstract (arXiv)

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.