c_EMC: Expectation Maximization Clustering

In egenn/rtemis: Machine Learning and Visualization

Expectation Maximization Clustering

Description

Perform clustering by EM using EMCluster::emcluster

Usage

c_EMC(
  x,
  x.test = NULL,
  k = 2,
  lab = NULL,
  EMC = EMCluster::.EMC,
  verbose = TRUE,
  ...
)

Arguments

x	Input matrix / data.frame
x.test	Testing set matrix / data.frame
k	Integer: Number of clusters to get
lab	Vector, length NROW(x): Labels for semi-supervised clustering
EMC	List of control parameters for EMCluster::emcluster. Default = EMCluster::.EMC
verbose	Logical: If TRUE, print messages to screen
...	Additional parameters to be passed to EMCluster::emcluster

Details

First, EMCluster::simple.init(x, nclass = k) is run, followed by EMCluster::emcluster(x, emobj = emobj, assign.class = TRUE, ...)

This can be very slow.

Author(s)

E.D. Gennatas

See Also

Other Clustering: c_CMeans(), c_DBSCAN(), 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.