Hello Karuna,
Christian answer was great and very detailed.
One more approach you might want to try is using the K-medoids algorithm
instead of the K-means.
That can be used through the pam command (from the cluster package)
See more here:
http://stat.ethz.ch/R-manual/R-patched/library/cluster/htm
Dear Ms Karunambigai,
the kmeans algorithm depends on random initialisation.
There are two basic strategies that can be applied in order to make your
results reproducible:
1) Fix the random number generator by means of set.seed (see ?set.seed)
before you run kmeans. The problem with this is tha
hi r-help,
i am doing kmeans clustering in stats. i tried for five clusters clustering
using:
kcl1 <- kmeans(as1[,c("contlife","somlife","agglife","sexlife",
"rellife","hordlife","doutlife","symtlife","washlife",
"chcklife","rptlife","countlife","colt
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