# using “princomp” in matlab for clustering

I have a set of matrices which should fall into 3 distinct set/groups/clusters. They are unlabelled. I wish to do unsupervised clustering with PCA. I am using matlab as well. At the end I would also like to examine the eigenvectors.

Matlab has a function call "princomp" which I believe can do this task; is this correct?

When I give "princomp" a matrix the output can be interpreted how?

For example:

dataTmp=[1 1; 2 2; 1 2; 2 3; 4 6; -1 1; -2 2; -4 3; -5 8]
dataTmp =
1     1
2     2
1     2
2     3
4     6
-1     1
-2     2
-4     3
-5     8

princomp(dataTmp)

ans =

0.9207    0.3902
-0.3902    0.9207


or should I being using the function "zscore" beforehand to standardise the values first?

princomp(zscore(dataTmp))

ans =

0.7071    0.7071
-0.7071    0.7071


How do I interpret the answer? The data I made were simple points in either the first or second quandrant.

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