## Usage

hkmeans(x, k, hc.metric = "euclidean", hc.method = "ward.D2", iter.max = 10, km.algorithm = "Hartigan-Wong")

"print"(x, ...)

hkmeans_tree(hkmeans, rect.col = NULL, ...)

## Arguments

x

a numeric matrix, data frame or vector

k

the number of clusters to be generated

hc.metric

the distance measure to be used. Possible values are "euclidean", "maximum", "manhattan",
"canberra", "binary" or "minkowski" (see ?dist).

hc.method

the agglomeration method to be used. Possible values include "ward.D", "ward.D2", "single",
"complete", "average", "mcquitty", "median"or "centroid" (see ?hclust).

iter.max

the maximum number of iterations allowed for k-means.

km.algorithm

the algorithm to be used for kmeans (see ?kmeans).

...

others arguments to be passed to the function plot.hclust(); (see ? plot.hclust)

hkmeans

an object of class hkmeans (returned by the function hkmeans())

rect.col

Vector with border colors for the rectangles around clusters in dendrogram