I am attempting to apply k-means on a set of high-dimensional data points (about 50 dimensions) and was wondering if there are any implementations that find the optimal number of clusters.
I remember reading somewhere that the way an algorithm generally does this is such that the inter-cluster distance is maximized and intra-cluster distance is minimized but I don't remember where I saw that. It would be great if someone can point me to any resources that discuss this. I am using SciPy for k-means currently but any related library would be fine as well.
If there are alternate ways of achieving the same or a better algorithm, please let me know.