sym.kmeans: Symbolic k-Means

In PROMiDAT/RSDA: R to Symbolic Data Analysis

Symbolic k-Means

Description

This is a function is to carry out a k-means overs a interval symbolic data matrix.

Usage

sym.kmeans(sym.data, k = 3, iter.max = 10, nstart = 1,
algorithm = c('Hartigan-Wong', 'Lloyd', 'Forgy', 'MacQueen'))

Arguments

sym.data	Symbolic data table.
k	The number of clusters.
iter.max	Maximun number of iterations.
nstart	As in R kmeans function.
algorithm	The method to be use, as in kmeans R function.

Value

This function return the following information:

K-means clustering with 3 clusters of sizes 2, 2, 4

Cluster means:

GRA FRE IOD SAP

1 0.93300 -13.500 193.500 174.75

2 0.86300 30.500 54.500 195.25

3 0.91825 -6.375 95.375 191.50

Clustering vector:

L P Co S Ca O B H

1 1 3 3 3 3 2 2

Within cluster sum of squares by cluster:

[1] 876.625 246.125 941.875

(between_SS / total_SS = 92.0

Available components:

[1] 'cluster' 'centers' 'totss' 'withinss' 'tot.withinss' 'betweenss'

[7] 'size'

Author(s)

Oldemar Rodriguez Rojas

References

Carvalho F., Souza R.,Chavent M., and Lechevallier Y. (2006) Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognition Letters Volume 27, Issue 3, February 2006, Pages 167-179

See Also

sym.hclust

Examples

data(oils)
sk <- sym.kmeans(oils, k = 3)
sk$cluster

PROMiDAT/RSDA documentation built on Sept. 14, 2023, 9:16 p.m.