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it sounds like you're doing two things, but really you're just doing one:
you could think of the feature derived from this model as something which has to be compared to clustering results in some sort of ensemble fashion. I think a better approach would be to use the other models output as one of the inputs to the clustering algorithm. Then you can tune the ...
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You can either use a sentence embedding model to associate a vector to each of your inputs, and use a clustering algorithm like KMeans, or build a similarity matrix between your strings using a string distance metric, and use a similarity-based algorithm like Spectral Clustering or Agglomerative Clustering.
The first one using KMeans might not work the best ...
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The intuition is to choose a point that is as far as possible from the existing centers. It does not matter in which direction the new point lies, as long as it is far away.
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