基于拓扑结构的分层聚类，可处理任意形状簇与异常点。
Hierarchical topological clustering
- 利用数据拓扑结构构建分层聚类，支持任意距离度量。
- 能有效识别任意形状的簇和异常点，提升复杂数据聚类效果。
- 适合处理图像、医疗、经济等高复杂度数据，对传统方法失效场景有效。
拓扑方法无需假设数据结构即可探索数据分布。本文提出一种分层拓扑聚类算法，可适配任意距离度量，从生成的层级结构中推断出异常点和任意形状簇的持续性。我们在若干数据集上验证了该方法的潜力，包括图像、医学和经济数据，其中异常点起关键作用。在其他方法失效的情况下，该方法仍能提供有意义的聚类结果。
原文摘要 · Abstract (English)
Topological methods have the potential of exploring data clouds without making assumptions on their the structure. Here we propose a hierarchical topological clustering algorithm that can be implemented with any distance choice. The persistence of outliers and clusters of arbitrary shape is inferred from the resulting hierarchy. We demonstrate the potential of the algorithm on selected datasets in which outliers play relevant roles, consisting of images, medical and economic data. These methods can provide meaningful clusters in situations in which other techniques fail to do so.
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