## Usage

hcut(x, k = 2, isdiss = inherits(x, "dist"), hc_func = c("hclust", "agnes", "diana"), hc_method = "ward.D2", hc_metric = "euclidean", stand = FALSE, graph = FALSE, ...)

## Arguments

x

a numeric matrix, numeric data frame or a dissimilarity matrix.

k

the number of clusters to be generated.

isdiss

logical value specifying wether x is a dissimilarity matrix.

hc_func

the hierarchical clustering function to be used. Default value is "hclust". Possible values
is one of "hclust", "agnes", "diana". Abbreviation is allowed.

hc_method

the agglomeration method to be used (?hclust) for hclust() and agnes():
"ward.D", "ward.D2", "single", "complete", "average", ...

hc_metric

character string specifying the metric to be used for calculating
dissimilarities between observations. Allowed values are those accepted by the function dist() [including "euclidean", "manhattan", "maximum",
"canberra", "binary", "minkowski"] and correlation based distance measures ["pearson", "spearman" or "kendall"].

stand

logical value; default is FALSE. If TRUE, then the data will be standardized using the function scale().
Measurements are standardized for each variable (column), by subtracting the variable's mean value and
dividing by the variable's standard deviation.

graph

logical value. If TRUE, the dendrogram is displayed.