c_PAM: Partitioning Around Medoids

In egenn/rtemis: Machine Learning and Visualization

Partitioning Around Medoids

Description

Perform PAM clustering using cluster::pam

Usage

c_PAM(
  x,
  k = 2,
  diss = FALSE,
  metric = "euclidean",
  do.swap = TRUE,
  verbose = TRUE,
  ...
)

Arguments

x	Input matrix / data.frame
k	Integer: Number of clusters to get
diss	Logical: If TRUE, x should be a dist or dissimilarity matrix. Otherwise, x should be a matrix of cases by features. Default = FALSE
metric	Character: Dissimilarity metric to be used. Options: 'euclidean', 'manhattan'
do.swap	Logical: If TRUE, perform the swap phase (See cluster::pam), as in the original PAM algorithm. This is computationally intensive and can be skipped. Default = TRUE
verbose	Logical: If TRUE, print messages to screen
...	Additional parameters to be passed to cluster::pam

Author(s)

E.D. Gennatas

See Also

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

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