# Validation of clustering results

I have a data which contains several columns which I later reduced using a PCA algorithms to two different components. I then applied the k-means algorithms to the data.
Now, how can I verify that my data clustered well into each group? Or how do I determine misclassification rate?

For instance, using R, if I check the cluster vector say k\$cluster against the labels of the data I had previously before clustering can I just draw a confusion matrix from that and assume that 1 in the clustered vector is equivalent to 1 in my labels?

col3    col2     Col1   lables
123     2.32      2.50    0
124    2.81      3.10     1
125    2.72      3.09     2
126    2.92      3.03     3
127    2.32      2.95     4


Please note this is a hypothetical data; my data is way bigger than this.

• You're speaking of assessing "misclassification". Do you mean that you had some classification of observations prior clustering, and now you want to compare that classification to the one given by clustering? – ttnphns Sep 15 '11 at 6:08
• yes that is what I know. – persistence911 Sep 15 '11 at 10:15
• Are you sure the cluster labels correspond to each other? I.e. that cluster A is cluster A in both custerings? And do you have equal number of clusters on the first place? – user88 Sep 15 '11 at 11:19
• There is a prior classification and One of the things I am confused about is can I safely assumes that A cluster vector in 1 generated after clustering will be equal to my label 1. Or How can i know if the cluster classification corresponds a little to my previous labels prior to clustering. – persistence911 Sep 15 '11 at 11:26
• For a real example of scrambled clusters, see how-to-calculate-classification-error-rate on SO. – denis Apr 19 '12 at 15:00

• @micans, do you have any idea if there is any implementation of VI criteria in R? if yes what package? – doctorate Nov 16 '13 at 14:05