Applying k-means over PCA I have a dataset containing 20 columns and 200 rows. This is an unlabeled dataset and I applied PCA to this dataset for dimensionality reduction. After successfully using PCA, I received a dataset with 2 columns(PC1 & PC2) and 200 rows. Then I executed the k-means algorithm on the output dataset(200x2). In the end, I got a graph plotting 5 clusters(X-axis is PC1 and Y-axis is PC2). But, how can we understand what X and Y axis on the graph represents? Also, how is it possible to figure out the understanding from this final graph when I don't have any clue about the values I obtained from PCA?

Your first question: With 20 columns in your dataset, it is probably difficult to "understand" what the two first PCs "mean" unless they happen to be very easy linear combinations of only few of your features. But it doesn't hurt to check. Your PCA algorithm should give you the linear combination of the first PCs from your features.
Your second question: This method is mainly giving you the clustering, i.e. the grouping of your data. Knowing which observations "belong together" is often quite valuable. Gleaning additional information from e.g. the position of the clusters in your PCA subspace is probably rather involved.
Also note, that PCA preprocessing can change the true clustering of your data quite a bit. E.g. it could easily happen that the projection to your two-dimensional PCA subspace maps two perfectly separated clusters into a single cluster. But you probably have your reasons why you do PCA.
