22/10/2025, 17:00 — 18:00 — Online
Sayeh Khaniha, Universidade de São Paulo
Hierarchical Clustering Algorithms on Poisson and Cox Point Processes
Clustering is a widely used technique in unsupervised learning for identifying groups within a dataset based on similarities among its elements. In this talk, I will introduce a novel hierarchical clustering model specifically designed for datasets with a countably infinite number of points. The proposed algorithm constructs clusters at successive levels using nearest-neighbor chains of points or clusters. We apply this algorithm to the Poisson point process and show that it defines a phylogenetic forest that is a factor of the process and, consequently, unimodular. We then study various properties of this random forest, including the mean cluster size at each level and the mean size of the cluster containing a typical node.
