I am running a K-means algorithm (using the
sklearn implementation) on an aggregated dataset of ~350k datapoints on a 6 dimension hyper-plane (using 6 features).
I would like to do the same but in the "non-aggregated" version of my dataset, which is ~1b datapoints using the same 6 features
I know this is a very heavy task for K-means, the number of datapoints is just too big, even though the dimensions' size is pretty small.
Are there any suggestions of other algorithms that would help me on this task, apart from mini batch K-means ?