clustering: Clustering maps of a conceptmaps object

In comato: Analysis of Concept Maps and Concept Landscapes

Description

clustering is a convenience function that implements two frequently used ways of clustering conceptmaps directly. The first is clustering using the MBMM algorithm and the concept matrix, the second is clustering using the PAM algorithm and the graph similarity matrix.

Usage

clustering(maps, method = c("MBMM", "PAM"), min = 1, max = 10)

Arguments

maps	A conceptmaps object.
method	Either "PAM" or "MBMM", indicating which algorithm should be used.
min	The minimal number of components that is tested. For the PAM algorithm, 1 is not allowed.
max	The maximal number of components that is tested.

Value

The return value of either MBM.cluster or PAM.cluster, depending on the value of method.

Examples

## Not run: 
#Assuming that there are concept maps in folder "~/maps"
cms = read.folder.tgf("~/maps")

clustering(cms, method="MBMM")

## End(Not run)

comato documentation built on May 2, 2019, 6:52 a.m.