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?

Thanks in advance.

Uli

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?