面向多维投影中可靠聚类分析的失真感知刷选技术
Distortion-Aware Brushing for Reliable Cluster Analysis in Multidimensional Projections
AI总结：
针对多维投影中传统刷选因投影失真导致聚类分析不可靠的问题，本文提出失真感知刷选技术，通过动态调整点位置校正失真，经用户研究和用例验证可提升聚类选择准确性与鲁棒性。
摘要：
刷选是二维散点图中常用的交互技术，支持用户在连续封闭区域内选择成簇点以开展后续分析或筛选。但将传统刷选应用于多维（MD）数据的二维表示形式——即多维投影（MDPs）时，MDP带来的失真会导致原始多维数据的聚类结构被不准确呈现，进而造成不可靠的聚类分析结果。为缓解该问题，本文提出一种面向MDP的新型刷选技术，命名为Distortion-aware brushing（失真感知刷选）。当用户执行刷选操作时，该技术会动态调整投影中的点位置，对当前刷选点周边的失真进行校正：将多维空间中与刷选点相近的数据点拉近，同时将距离较远的点推远。这种动态调整可帮助用户更精准地刷选多维簇，得到更可靠的聚类分析结果。我们开展了包含24名参与者的用户研究，结果显示Distortion-aware brushing在多维空间中精准分离簇的表现显著优于现有MDP刷选技术，且对失真具备鲁棒性。我们还通过两个用例进一步验证了该技术的有效性：一是地理空间数据的聚类分析，二是多维簇的交互式标注。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
