opticskxi: OPTICS k-Xi clustering algorithm

In ThomasChln/opticskxi: OPTICS K-Xi Density-Based Clustering

OPTICS k-Xi clustering algorithm

Description

For each largest distance differences on the OPTICS profile, consecutive observations left and right on the OPTICS profile (i.e. lower and higher OPTICS id) will be assigned to 2 different clusters if their distance is below the distance of the edge point. If above, observations are NA. The pts parameter defines a minimum number of observations to form a valley (i.e. cluster). If the number of observations in one valley is smaller than pts, observations are set to NA.

Usage

opticskxi(
  optics_obj,
  n_xi,
  pts = optics_obj$minPts,
  max_loop = 50,
  verbose = FALSE
)

Arguments

optics_obj	Data frame returned by optics
n_xi	Number of clusters to define
pts	Minimum number of points per clusters
max_loop	Maximum iterations to find n_xi clusters
verbose	Print the ids of the largest difference considered and cluster information if they define one

Value

Vector of clusters

See Also

opticskxi_pipeline, ggplot_optics

Examples

data('multishapes')
optics_shapes <- dbscan::optics(multishapes[1:2])
kxi_shapes <- opticskxi(optics_shapes, n_xi = 5, pts = 30)
ggplot_optics(optics_shapes, groups = kxi_shapes)
ggpairs(cbind(multishapes[1:2], kXi = kxi_shapes), group = 'kXi')

ThomasChln/opticskxi documentation built on April 12, 2025, 5:43 a.m.