DBSCAN

Density-based Spatial Clustering of Applications with Noise (DBSCAN) is a data clustering algorithm that finds clusters through density-based expansion of seed points. The algorithm was proposed in:

PhyloClustering.dbscan_label — Function

dbscan_label(tree::AbstractMatrix{<:Real}, radius::Real; min_neighbors::Int64 = 1, min_cluster_size::Int64 = 1)

Get predicted labels from density-based spatial clustering of applications with noise for a group of phylogenetic trees.

Arguments

tree: a B * N tree Matrix (each column of tree Matrix is a B-dimensional tree in bipartiton format) and B < N.
radius: neighborhood radius; points within this distance are considered neighbors.
min_neighbors: minimal number of neighbors required to assign a point to a cluster.
min_cluster_size: minimal number of points in a cluster.

Output

A Vector object with length of N containing predicted labels for each tree (the cluster it belongs to). 0 means the tree is noise.

source

Reference

The implementation of DBSCAN is provided by Clustering.jl.

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96). AAAI Press, 226–231.