I'm working with a dataset with latitude, longitude and date-time, and 5 million points per day. And I don't have an expected number of cluster, and depending on the day it should change.
I'm coding in Python, with a clickhouse database to store the source data.
==> Is there a way to do a spatiotemporal clustering that includes the 3 features?
So far I have scaled/normalized the 3 features and use MiniBatchKMeans (the current solution used), or a Euclidian distance, but I'm losing the notion of the physical distance between points.
DBSCAN or HDBSCAN with Havresine is accepting only 2 features (lat lon in radians).
Also, the volume rule-out non-optimized solution that doesn't scale (I’ve tried the ST-DBSCAN available on GitHub, I stopped it after the 15h run on just 2 hours of data).
I expect to get clusters of my datapoints regrouping nearest points in location and time together.