clust5

A dataset containing two variables of 800 objects and their class memberships
generated by the clusterGeneration package.

datasets

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 validation applies internal and
relative criteria. The internal criteria includes silhouette index and shadow
values. The relative criterium applies bootstrap procedure producing a heatmap
with a flexible reordering matrix algorithm such as complete, ward, or average
linkages. The cluster result can be plotted in a marked barplot or pca biplot.

Weksi Budiaji

kmed

Distance-Based k-Medoids

clust5 function

A data frame with 800 rows and 3 variables:<dl>
 <dt>x1</dt>
<dd>X1.</dd> <dt>x2</dt>
<dd>X2.</dd> <dt>class</dt>
<dd>Class membership.</dd>
</dl>

Format

5-clustered data set — clust5

A data frame with 800 rows and 3 variables:<dl>
 <dt>x1</dt>
<dd>X1.</dd>

 <dt>x2</dt>
<dd>X2.</dd>

 <dt>class</dt>
<dd>Class membership.</dd>


</dl>

clust5: 5-clustered data set

Description

Usage

Arguments

Format

References