c_HOPACH: Hierarchical Ordered Partitioning and Collapsing Hybrid

In egenn/rtemis: Machine Learning and Visualization

Hierarchical Ordered Partitioning and Collapsing Hybrid

Description

Perform HOPACH clustering using hopach::hopach

Usage

c_HOPACH(
  x,
  dmat = NULL,
  metric = c("cosangle", "abscosangle", "euclid", "abseuclid", "cor", "abscor"),
  k = 15,
  kmax = 9,
  khigh = 9,
  trace = 0,
  verbose = TRUE,
  ...
)

Arguments

x	Input matrix / data.frame
dmat	Matrix (numeric, no missing values) or hdist object of pairwise distances. If NULL, it is computed based on metric
metric	Character: Dissimilarity metric to be used. Options: "cosangle", "abscosangle", "euclid", "abseuclid", "cor", "abscor"
k	Integer, (0:15]: Maximum number of levels
kmax	Integer, [1:9]: Maximum number of children at each node in the tree
khigh	Integer, [1:9]: Maximum number of children at each nod ein the tree when computing the the Mean/Median Split Silhouette. Usually same as kmax
trace	Integer: If trace > 0, print messages during HOPACH run. Default = 0
verbose	Logical: If TRUE, print messages to console
...	Additional parameters to pass to cluster::hopach

Author(s)

E.D. Gennatas

See Also

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

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