Description Usage Arguments Value Examples
View source: R/sphere_gskmeans.R
Geodesic spherical k-means algorithm is an counterpart of the spherical k-means algorithm by replacing the cosine similarity with the squared geodesic distance, which is the great-circle distance under the intrinsic geometry regime on the unit hypersphere. If the data is not normalized, it performs the normalization and proceeds thereafter.
1 |
data |
an (n\times p) matrix of row-stacked observations. If not row-stochastic, each row is normalized to be unit norm. |
k |
the number of clusters (default: 2). |
... |
extra parameters including
|
a named list of S3 class T4cluster
containing
a length-n vector of class labels (from 1:k).
a value of the cost function.
an (k\times p) matrix where each row is a unit-norm class mean.
name of the algorithm.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # -------------------------------------------------------------
# clustering with 'household' dataset
# -------------------------------------------------------------
## PREPARE
data(household, package="T4cluster")
X = household$data
lab = as.integer(household$gender)
## EXECUTE GSKMEANS WITH VARYING K's
vec.rand = rep(0, 9)
for (i in 1:9){
clust_i = gskmeans(X, k=(i+1))$cluster
vec.rand[i] = compare.rand(clust_i, lab)
}
## VISUALIZE THE RAND INDEX
opar <- par(no.readonly=TRUE)
plot(2:10, vec.rand, type="b", pch=19, ylim=c(0.5, 1),
ylab="Rand index",xlab="number of clusters",
main="clustering quality index over varying k's.")
par(opar)
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