After running k-means (mllib spark scala) I want to make sense of the cluster centers I obtained from data which I pre-processed using (among other transformers) mllib's OneHotEncoder.
A center looks like this:
Cluster Center 0 [0.3496378699559276,0.05482645034473324,111.6962521358467,1.770525792286651,0.0,0.8561916265130964,0.014382183950365071,0.0,0.0,0.0,0.47699722692567864,0.0,0.0,0.0,0.04988557988346689,0.0,0.0,0.0,0.8981811028926263,0.9695107580117296,0.0,0.0,1.7505886931570156,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.771620072281845,0.0,0.0,0.0,0.0]
Which is obviously not very human friendly... Any ideas on how to revert the one-hot encoding and retrieve the original categorical features? What if I look for the data point which is closest (using the same distance metric that is used by k-means, which I assume is Euclidean distance) to the centroid and then revert the encoding of that particular data point?
For the cluster centroids it is not possible (strongly disrecommended) to reverse the encoding. Imagine you have the original feature "3" out of 6 and it is encoded as [0.0,0.0,1.0,0.0,0.0,0.0]. In this case it's easy to extract 3 as the correct feature from the encoding.
But after kmeans application you may get a cluster centroid that looks for this feature like this [0.0,0.13,0.0,0.77,0.1,0.0]. If you want to decode this back to the representation that you had before, like "4" out of 6, because the feature 4 has the largest value, then you will lose information and the model may get corrupted.
Edit: Add a possible way to revert encoding on datapoints from the comments to the answer
If you have IDs on the datapoints you can perform a select / join operation on the ID after you assigned a datapoints to a cluster to get the old state, before the encoding.