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.Q:

Tracking confidence of clustering when NAs are present

I am using a SAS macro to cluster training data using k-means. It’s working fine except that if any observations are missing, the “out-of-cluster” observations are treated as members of the “out-of-cluster” cluster. However, I want to track the confidence of each observation in the cluster. If the “out-of-cluster” observations are more likely due to the cluster, then I don’t want them to be assigned to the cluster.
I have an idea of how to do this by calculating the cluster mean and dividing the distance between each observation and the mean by the standard deviation of the cluster. Then, if that ratio is greater than some threshold, the observation is assigned to the cluster. Is this a good way to track the confidence?
As a sample data set, I’m using 5 clusters and 4 observations per cluster.
X,Y
10,12
10,13
10,11
10,20
10,12
10,11
10,10
10,11
10,12
NA,13
NA,14
10,12
10,14
10,11
10,15
10,14
10,16
NA,17
NA,18
10,11
10,15
10,11
10,12
10,15
10,16
10,17
10,18
10,19
10,20
10,20
10,12

A:

Your idea to score clustering output by computing ratios of distances to mean is not a

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