sym.kmeans {RSDA} | R Documentation |
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