一种基于网格的降维散点图布局重叠消除方法
A Grid-based Method for Removing Overlaps of Dimensionality Reduction Scatterplot Layouts
AI总结：
本文提出DGrid后处理方法，通过网格策略消除降维散点图重叠，保留原始布局特征并限定字形最小尺寸，在多项指标上超越现有技术且速度快，用户研究证实其视觉保真度与美观性。
摘要：
降维（DR）散点图布局已成为分析多维数据集的普遍可视化工具。尽管其广受欢迎，此类散点图仍存在遮挡问题，尤其是当使用信息丰富的字形来表示数据实例时，可能会模糊正在进行的分析中的关键信息。为应对这一问题，人们设计了多种策略，要么生成无重叠的布局，但这类布局缺乏当代降维技术在揭示有趣数据模式方面的强大能力；要么将消除重叠作为后处理策略。尽管后处理技术取得了良好效果，但大多数最佳方法通常会扩展或扭曲散点图区域，从而将字形尺寸（有时）缩小到不可读的程度，这反而违背了消除重叠的目的。本文提出了距离网格（DGrid），一种新颖的后处理策略，用于消除降维布局中的重叠，该策略忠实保留原始布局的特征，并限定最小字形尺寸。我们通过考虑多种不同指标的广泛比较评估表明，DGrid在重叠消除方面超越了现有最先进技术，同时还是最快的技术之一，尤其适用于大型数据集。一项涉及51名参与者的用户研究也表明，DGrid在保留原始散点图视觉特征和最终结果美观性方面始终位列顶尖技术之中。
英文摘要：
Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous visualization tool for analyzing multidimensional datasets. Despite their popularity, such scatterplots suffer from occlusion, especially when informative glyphs are used to represent data instances, potentially obfuscating critical information for the analysis under execution. Different strategies have been devised to address this issue, either producing overlap-free layouts that lack the powerful capabilities of contemporary DR techniques in uncovering interesting data patterns or eliminating overlaps as a post-processing strategy. Despite the good results of post-processing techniques, most of the best methods typically expand or distort the scatterplot area, thus reducing glyphs' size (sometimes) to unreadable dimensions, defeating the purpose of removing overlaps. This paper presents Distance Grid (DGrid), a novel post-processing strategy to remove overlaps from DR layouts that faithfully preserves the original layout's characteristics and bounds the minimum glyph sizes. We show that DGrid surpasses the state-of-the-art in overlap removal (through an extensive comparative evaluation considering multiple different metrics) while also being one of the fastest techniques, especially for large datasets. A user study with 51 participants also shows that DGrid is consistently ranked among the top techniques for preserving the original scatterplots' visual characteristics and the aesthetics of the final results.
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