Distortion-Aware Brushing for Interactive Cluster Analysis in Multidimensional Projections

2022-01-17

arXiv.org

AI Summary

In multidimensional projection (MDP), conventional brushing fails to reliably extract clusters due to projection distortion, which misrepresents high-dimensional clustering structure. This paper introduces Distortion-Aware Brushing—a novel interactive technique that dynamically embeds local distortion correction into the brushing process. Specifically, it realigns projected points on-the-fly using a neighborhood-based distortion metric, ensuring that proximal high-dimensional points coalesce and distant ones separate in the projection space. Coupled with high-dimensional distance-guided projection refinement and a lightweight interactive rendering framework, our method achieves semantic alignment between the 2D projection and the underlying high-dimensional data manifold. A user study (n=30) demonstrates that our approach significantly improves cluster separation accuracy and exhibits strong robustness across diverse distortion types and severities, enabling distortion-resilient, real-time clustering analysis.

Abstract

Brushing is an everyday interaction in 2D scatterplots, which allows users to select and filter data points within a continuous, enclosed region and conduct further analysis on the points. However, such conventional brushing cannot be directly applied to Multidimensional Projections (MDP), as they hardly escape from False and Missing Neighbors distortions that make the relative positions of the points unreliable. To alleviate this problem, we introduce Distortion-aware brushing, a novel brushing technique for MDP. While users perform brushing, Distortion-aware brushing resolves distortions around currently brushed points by dynamically relocating points in the projection; the points whose data are close to the brushed data in the multidimensional (MD) space go near the corresponding brushed points in the projection, and the opposites move away. Hence, users can overcome distortions and readily extract out clustered data in the MD space using the technique. We demonstrate the effectiveness and applicability of Distortion-aware brushing through usage scenarios with two datasets. Finally, by conducting user studies with 30 participants, we verified that Distortion-aware brushing significantly outperforms previous brushing techniques in precisely separating clusters in the MD space, and works robustly regardless of the types or the amount of distortions in MDP.

Problem

• Improves cluster analysis in multidimensional projections
• Reduces distortion effects in brushing techniques
• Enhances accuracy of cluster separation in MD space

Innovation

• Dynamic point relocation for distortion correction
• Proximity-based adjustment in multidimensional space
• Enhanced brushing accuracy for cluster analysis