<ul data-eligibleForWebStory="true"><li>Distortion-aware brushing is introduced to address the issue of unreliable cluster analysis in multidimensional projections caused by distortions in the data representation.</li><li>Conventional brushing in 2D scatterplots may lead to inaccuracies in cluster analysis when applied to multidimensional data projections.</li><li>The new technique, Distortion-aware brushing, corrects distortions around brushed points by adjusting the points in the projection dynamically.</li><li>This adjustment pulls close points together and pushes distant points apart in the multidimensional space, enhancing the accuracy of cluster brushing.</li><li>User studies involving 24 participants demonstrate that Distortion-aware brushing outperforms previous techniques in separating clusters accurately and remains robust against distortions.</li><li>The effectiveness of the technique is showcased through two use cases: cluster analysis of geospatial data and interactive labeling of multidimensional clusters.</li></ul>

Distortion-Aware Brushing for Reliable Cluster Analysis in Multidimensional Projections

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