Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires
Fig 9
HDBSCAN density-based clustering.
Clusters are found by generating a graphical representation of data, and then clustering on the graph. The data shown in this figure are from the latent projections from Fig 1. Notably, the three clusters in Fig 1. are clustered into only two clusters using HDBSCAN, exhibiting a potential shortcoming of the HDBSCAN algorithm. The grey colormap in the condensed trees represent the number of points in the branch of the tree. Λ is a value used to compute the persistence of clusters in the condensed trees.