Cluster doc2vec using Affinity Propagation I want to cluster my document vectors (doc2vec) using affinity propagation.

However, I am just confused if I should use cosine similarity or cosine distance to cluster my document vectors. Currently, I am using cosine similarity for my affinity propagation clustering. Thus, my first question is;

Is it correct to use cosine similarity to cluster my doc2vec document vectors?

Moreover, I would like to visualize my cluster results using t-sne. However, I saw that t-sne requires distance matrix as the input. Hence, my second question is;

Is it correct to use distance matrix (cosine distance) for t-sne, while I use cosine similarity for clustering?

If my code is required I can post it too.

Please help me.
Both of them convert the distances back to similarities, albeit using different methods. They will, if I recall correctly, also square the distances.

This may be problematic with the most common variant of cosine distance, which already is a squared distance. So it may be a good idea to modify the methods to be able to directly work with the similarities. But you probably need to modify the source code for this (and understand the methods!)