c_DBSCAN: Density-based spatial clustering of applications with noise

In egenn/rtemis: Machine Learning and Visualization

Density-based spatial clustering of applications with noise

Description

Perform DBSCAN clustering

Usage

c_DBSCAN(
  x,
  x.test = NULL,
  eps = 1,
  minPts = NCOL(x) + 1,
  weights = NULL,
  borderPoints = TRUE,
  search = c("kdtree", "linear", "dist"),
  verbose = TRUE,
  ...
)

Arguments

x	Input matrix / data.frame
x.test	Testing set matrix / data.frame
eps	Numeric: Radius of the epsilon neighborhood
minPts	Integer: Number of minimum points required in the eps neighborhood for core points (including the point itself).
weights	Numeric vector: Data points' weights. Needed for weighted clustering.
borderPoints	Logical: If TRUE, assign border points to clusters, otherwise they are considered noise
search	Character: "kdtree", "linear" or "dist": nearest neighbor search strategy
verbose	Logical: If TRUE, print messages to screen
...	Additional parameters to be passed to flexclust::cclust

Details

See dbscan::dbscan for info on how to choose eps and minPts

Author(s)

Efstathios D. Gennatas

See Also

Other Clustering: c_CMeans(), c_EMC(), c_H2OKMeans(), c_HARDCL(), c_HOPACH(), c_KMeans(), c_MeanShift(), c_NGAS(), c_PAM(), c_PAMK(), c_SPEC()

egenn/rtemis documentation built on Oct. 28, 2024, 6:30 a.m.