用二维密度图快速聚类嵌入点，秒级生成标签
A Scalable Approach to Clustering Embedding Projections
- 在投影二维空间中用核密度估计替代点聚类
- 数百毫秒内完成高质量聚类，比现有方法快数个数量级
- 适合需要实时交互可视化的数据探索场景
嵌入投影的交互式可视化有助于理解数据和评估机器学习模型。在这些可视化中对数据打标签对于解释结果至关重要，标签能提供投影概览并引导用户导航。然而，大多数标签生成方法依赖于对点进行聚类，随着点数增加，计算成本显著上升。本文提出一种高效聚类方法：在二维投影空间中使用核密度估计生成密度图，进而提取聚类区域。该算法可在数百毫秒内从2D密度图生成高质量聚类区域，相比当前方法快数个数量级。我们贡献了算法设计、基准测试及应用实例，展示了其在标签生成与数据摘要中的实用性。
原文摘要 · Abstract (English)
Interactive visualization of embedding projections is a useful technique for understanding data and evaluating machine learning models. Labeling data within these visualizations is critical for interpretation, as labels provide an overview of the projection and guide user navigation. However, most methods for producing labels require clustering the points, which can be computationally expensive as the number of points grows. In this paper, we describe an efficient clustering approach using kernel density estimation in the projected 2D space instead of points. This algorithm can produce high-quality cluster regions from a 2D density map in a few hundred milliseconds, orders of magnitude faster than current approaches. We contribute the design of the algorithm, benchmarks, and applications that demonstrate the utility of the algorithm, including labeling and summarization.
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