Unsupervised space–time clustering using persistent homology

This paper presents a new clustering algorithm for space–time data based on the concepts of topological data analysis and, in particular, persistent homology. Employing persistent homology—a flexible mathematical tool from algebraic topology used to extract topological information from data—in unsupervised learning is an uncommon and novel approach. A notable aspect of this methodology consists in analyzing data at multiple resolutions, which allows for distinguishing true features from noise based on the extent of their persistence. We evaluate the performance of our algorithm on synthetic data and compare it to other well‐known clustering algorithms such as K‐means, hierarchical clustering, and DBSCAN (density‐based spatial clustering of applications with noise). We illustrate its application in the context of a case study of water quality in the Chesapeake Bay.