Nothing
library(gmum.r)
data(cec.mouse1.spherical)
dataset = cec.mouse1.spherical
# That is the dataset we want to cluster:
plot(dataset, main="Mouse-like dataset")
# Run CEC with default parameters. Set the number of clusters and the dataset.
c <- CEC(k=3, x=dataset)
plot(c)
# Since initial clusterization is random. It may be a good idea to run CEC multiple times and choose the best result.
c <- CEC(k=3, x=dataset, control.nstart=10)
plot(c)
# Better than before, however, we know that clusters are spherical; let's inform CEC about that.
c <- CEC(k=3, x=dataset, control.nstart=10, method.type='spherical')
plot(c)
# Learn details of clustering:
c$centers
c$covMatrix
# Predict cluster which a point would belong to:
predict(c, c(1,1))
# Visualise size and shape of clusters:
plot(c, ellipses=TRUE)
# Try the same with random assignment.
c <- CEC(k=3, x=dataset, control.nstart=10, method.type='spherical', method.init='random')
plot(c)
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