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I have 150 images, 15 each of 10 different people. So basically I know which image should belong together, if clustered.

These images are of 73 dimensions (feature-vector) and I clustered them into 10 clusters using kmeans function in matlab.

Later, I processed these 150 data points and reduced its dimension from 73 to 3 for my work and applied the same kmeans function on them.

I want to compare the results obtained on these data sets (processed and unprocessed) by applying the same k-means function and wish to know if the processing which reduced it to lower dimension improves the kmeans clustering or not.

I thought comparing the variance of each cluster can be one parameter for comparison, however I am not sure if I can directly compare and evaluate my results (within cluster sum of distances etc.) as both the cases are of different dimension. Could anyone please suggest a way where I can compare the kmean results, some way to normalize them or any other comparison that I can make?

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"External" evaluation measures such as the adjusted Rand index (ARI) can be used to:

  • compare your clustering result to you original labels (people)
  • compare two results with each other for similarity
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