# Distortion-Aware Brushing for Reliable Cluster Analysis in Multidimensional Projections

arXiv: https://arxiv.org/abs/2201.06379 · HTML: https://mlsanity.com/paper/2201.06379/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 83 references that are in the map.
- First posted on arXiv 17 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Rates of Convergence for Nearest Neighbor Classification](https://mlsanity.com/paper/1407.0067/) (2014)
- [Revisiting Dimensionality Reduction Techniques for Visual Cluster Analysis: An Empirical Study](https://mlsanity.com/paper/2110.02894/) (2021)
- [Clustrophile 2: Guided Visual Clustering Analysis](https://mlsanity.com/paper/1804.03048/) (2018)
- [Measuring and Explaining the Inter-Cluster Reliability of Multidimensional Projections](https://mlsanity.com/paper/2107.07859/) (2021)
- [Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology](https://mlsanity.com/paper/2007.13872/) (2020)
- [Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction](https://mlsanity.com/paper/2110.00317/) (2021)

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

- [ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddings](https://mlsanity.com/paper/2308.00282/) (2023)
- [Classes are not Clusters: Improving Label-based Evaluation of Dimensionality Reduction](https://mlsanity.com/paper/2308.00278/) (2023)
- [CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering](https://mlsanity.com/paper/2308.00284/) (2023)

## Abstract (arXiv)

Brushing is a common interaction technique in 2D scatterplots, allowing users to select clustered points within a continuous, enclosed region for further analysis or filtering. However, applying conventional brushing to 2D representations of multidimensional (MD) data, i.e., Multidimensional Projections (MDPs), can lead to unreliable cluster analysis due to MDP-induced distortions that inaccurately represent the cluster structure of the original MD data. To alleviate this problem, we introduce a novel brushing technique for MDPs called Distortion-aware brushing. As users perform brushing, Distortion-aware brushing corrects distortions around the currently brushed points by dynamically relocating points in the projection, pulling data points close to the brushed points in MD space while pushing distant ones apart. This dynamic adjustment helps users brush MD clusters more accurately, leading to more reliable cluster analysis. Our user studies with 24 participants show that Distortion-aware brushing significantly outperforms previous brushing techniques for MDPs in accurately separating clusters in the MD space and remains robust against distortions. We further demonstrate the effectiveness of our technique through two use cases: (1) conducting cluster analysis of geospatial data and (2) interactively labeling MD clusters.