For vanilla K-Means clustering algorithm I know that the time complexity is:
Time complexity: O(tknm),
where n is the number of data points, k is the number of clusters, and t is the number of iterations, m is the dimensionality of the vectors.
So, when I studied about Mini-batch K-Means to make the algorithm converge faster, I wanted to find out what is the Space & Time complexity of it?
Essentially so that I understand well, how much we are optimizing over vanilla K-Means.