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Cluster analysis is the task of partitioning data into subsets of objects according to their mutual "similarity," without using preexisting knowledge such as class labels. [Clustered-standard-errors and/or cluster-samples should be tagged as such; do NOT use the "clustering" tag for them.]
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Under what conditions would clustering on top of Principal Components would return different...
Since Principal components capture most of the information, clustering on them should provide similar result as that of the clustering on the original data. … But are there situations where clustering on PCs may not be as good and may provide worse results than the original data set? …