I am looking to divide an area into n clusters. The dataset consists of stores with coordinates and their revenue. However I do not know which clustering algorithm I should use.
After some research I always end up with K means, which does an okay job with just the coordinates as features, even with Euclidean distances, however, of course when I add another non location based feature the centroids are not location centroids.
To include revenue as a feature I have multiplied the store data points by a revenue based factor, to increase point density in an area with a higher relative area.
Should I try to redistribute the Kmeans data points to create evenly sized cluster?(Even similar sized clusters based on amount of stores only would also already help a lot) If so how, because then Euclidean distance doesn't make sense anymore to assign it to the nearest cluster.
Or should I use something like DBSCAN, which I've tried, but not managed to fit to my coordinate data yet.