stepkmed

A matrix of distance objects (n x n) or a diss class.

distdata

ncluster

A number of iterations for the clustering algorithm.

iterate

A numeric number to determine the range of initial medoids
selection.

alpha

This function is deprecated, use the <code><a rd-options="" href="/link/inckmed?package=kmed&version=0.3.0" data-mini-rdoc="kmed::inckmed">inckmed</a></code>
function instead.

Algorithms of distance-based k-medoids clustering:
simple and fast k-medoids, ranked k-medoids, and increasing number
of clusters in k-medoids. Calculate distances for mixed variable
data such as Gower, Podani, Wishart, Huang, Harikumar-PV, and
Ahmad-Dey. Cluster validations apply internal and relative
criteria. The internal criteria include silhouette index and
shadow values. The relative criterium applies bootstrap procedure
producing a heatmap with a flexible reordering matrix algorithm
such as ward, complete, or centroid linkages. The cluster result
can be plotted in a marked barplot or pca biplot.

Weksi Budiaji

kmed

Distance-Based K-Medoids

stepkmed function

This function is deprecated, use the <code><a rd-options='' href='inckmed'>inckmed</a></code>
function instead.

stepkmed: Step k-medoid algorithm from Yu et al.

Description

Usage

Arguments