c_HARDCL: Clustering by Hard Competitive Learning

In egenn/rtemis: Machine Learning and Visualization

Clustering by Hard Competitive Learning

Description

Perform clustering by Hard Competitive Learning using flexclust::cclust

Usage

c_HARDCL(x, x.test = NULL, k = 2, dist = "euclidean", verbose = TRUE, ...)

Arguments

x	Input matrix / data.frame
x.test	Optional test set data
k	Integer: Number of clusters to get
dist	Character: Distance measure to use: 'euclidean' or 'manhattan'
verbose	Logical: If TRUE, print messages to console
...	Additional parameters to be passed to flexclust::cclust

Author(s)

E.D. Gennatas

See Also

Other Clustering: c_CMeans(), c_DBSCAN(), c_EMC(), c_H2OKMeans(), 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.