kdbscan: Kernel density based 2d clustering.

In OuNao/sumapc: Sequential UMAP Clustering

Kernel density based 2d clustering.

Description

Kernel density based 2d clustering.

Usage

kdbscan(
  x,
  minpts = 100,
  mindens = 0.001,
  maxlvl = 100,
  alfa = 0.05,
  theta = 5,
  ret_model = FALSE
)

Arguments

x	a data matrix.
minpts	min cluster size.
mindens	density cutoff.
maxlvl	max sequential level of space partitioning.
alfa	numeric. Exclude alfa portion of each extremity before search for a cut point in density curve.
theta	integer. Angle of rotation in each step.
ret_model	logical.

Value

A vector with cluster numbers. Length = nrow(x)

OuNao/sumapc documentation built on May 5, 2024, 12:35 p.m.