I do not really understand your question. You can use use kmeans but
without the observations that include the NA values (e.g. by deleting
whole rows in your observation matrix). If you want to keep the
information in the valid observations of those rows, I fear you need to
look for a clustering algorithm that can handle missing values. I doubt
that there is a kmeans version that can. Think about inserting means of
all other observations into the gaps, though this introduces bias as well.
Jannis
Raji schrieb:
Hi,
I am using k means algorithm for clustering.My data contains a few null/NA
values.kmeans doesnt cluster with those values.Are there any option like
na.omit which can avoid these null values and cluster the remaining values?
Thanks,
Raji
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