I have a data set with labels that were produced by a $k$-means clustering algorithm. Now there is some data (with the same data structure) from another source and I wonder what is the most sensible way to label this new, yet unseen data? I was thinking about either
- calculating the distance to the prior $k$-means centroids and label the data to the the nearest centroids accordingly
- run a new algorithm (e.g. SVM) on the new data using the old data as the training set
Unfortunately, I couldn't find anything about this particular problem. There are only a few questions about the general use of $k$-means as a classification model:
- Can $k$-means clustering do classification?
- How to segment new data with existing $k$-means model?