# Clustering Application with a Huge Number of Clusters

I am wondering if there are any clustering applications in practice where the number of clusters, i.e., the $$k$$ in the $$k$$-means problem is very high ($$k>50$$, optimally $$k>200$$), if possible with a citation.

The clustering can take place in any metric space.

In Learning Feature Representations with K-means' [1] the authors apply unsupervised learning, i.e., k-means to learn 256 features (centroids, centers). They also claim that the huge number of centers, in this case, k=256 is a limitation to this approach.