hclust {SPARTAAS}

Hierarchical clustering of up to two datasets (Compromised clustering).

Description

Overload of hclust for dealing with two dissimilarities matrices. Hierarchical cluster analysis on a set of dissimilarities and methods for analyzing it.

Usage

hclust(d, method = "complete", members = NULL, d2 = NULL, alpha = NULL)

Arguments

d

a dissimilarity structure as produced by dist.

method

the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).

members

NULL or a vector with length size of d. See the ‘Details’ section.

d2

a second dissimilarity structure as produced by dist.

alpha

The mixing parameter in order to generate the D_alpha matrix on which the classical hclust method is applied. Formula: D_alpha = alpha * d + (1-alpha) * d2.

Details

Data fusion (parameter alpha: optimal value see hclustcompro_select_alpha). It is necessary to define the appropriate proportion for each data source. This is the first sensitive point of the method that the user has to consider. A tool is provided to help him in his decision.

Value

hclust

Author(s)

The hclust function is based on Fortran code contributed to STATLIB by F. Murtagh.

A. COULON

L. BELLANGER

P. HUSI

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (d, method = "complete", members = NULL, d2 = NULL,
    alpha = NULL)
{
    if (!is.null(d2)) {
        if (!length(d) == length(d2)) {
            stop("d and d2 have not the same size.")
        }
        if (is.null(alpha)) {
            sa <- hclustcompro_select_alpha(d, d2, method = method,
                resampling = FALSE)
            alpha <- sa$alpha[1]
        }
        alpha <- as.numeric(alpha)
        if (!(alpha > 0 & alpha < 1)) {
            warning("Alpha must be between 0 and 1.")
            sa <- hclustcompro_select_alpha(d, d2, method = method,
                resampling = FALSE)
            alpha <- sa$alpha[1]
        }
        d <- dist(alpha * d + (1 - alpha) * d2)
    }
    stats::hclust(d, method, members)
  }