Dear All,

I'm working with a dataset with latitude, longitude and date-time, and 5 million points per day.

And I'm coding in Python, with a clickhouse database to store the source data.

Is there a way to do a spatio-temporal clustering that includes the 3 features?

So far I have scaled/normalized the 3 features and use MiniBatchKMeans (current solution used), or an Euclidian distance, but I 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 don't scale (I’ve tried the ST-DBSCAN available on GitHub, I stoped it after 15h run on just 2 hours of data).

And I don't have and expected number of cluster, and depending on the day it should change.

Do you have a better idea?

Thanks