Hi Mo, thanks for this work. It seems interesting.
I had the chance to play a little bit and wanted to compare that with KMeans. I relied on sklearn KMeans implementation.
Furthermore, I did some examples (mostly what is available). But One interesting thing I did is I generated some isotropic Gaussian blobs for clustering (using `make_blobs`) and then tried a comparison between the two methods. Bandit PAM was a little bit better for a couple of metrics I used, but also much faster. I was generating `n_samples=1000` but then I increased it to `n_samples=10000` and I found that it is much slower than KMeans, see [1] and code is in [2]. Is there a particular reason for that?
I had the chance to play a little bit and wanted to compare that with KMeans. I relied on sklearn KMeans implementation.
Furthermore, I did some examples (mostly what is available). But One interesting thing I did is I generated some isotropic Gaussian blobs for clustering (using `make_blobs`) and then tried a comparison between the two methods. Bandit PAM was a little bit better for a couple of metrics I used, but also much faster. I was generating `n_samples=1000` but then I increased it to `n_samples=10000` and I found that it is much slower than KMeans, see [1] and code is in [2]. Is there a particular reason for that?
[1] https://imgur.com/a/VibpgNz
[2] https://paste.elashri.xyz/aXCE