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.