I'm doing clustering of documents by applying k-Means on the word-vectors. To measure the cluster quality, I calculate David Bouldin Index for different k's. I tried two different distance measures, Cosine Similarity and Manhatten Distance, and get quite different values:
- Cosine Similarity: ~0.8 to ~0.6
- Manhattan Distance: ~0.3 to ~0.2
Can these values be compared directly? (Does Manhattan really performs a lot better here?) Or is there another way to compare the clustering results of the two different measures?