k means clustering with fixed constraints (sum of specific attribute should be less than or equal 90,000)
A turnkey solution will not work for you.
You'll have to formulate this as a standard constraint optimization problem and run a silver to optimize this. It's fairly straightforward: take the k-means objective and add your constraints...
Suppose I have 20,000 features on map, and each feature have many attributes (as well as the latitude and longitude). One of the attributes called population.
I want to split these 20,000 features into 3 clusters where the total sum of population of each cluster are equal to specific value 90,000 and features in each cluster should be near each others(ie will take locations in our consideration)
So, the output clusters should have the following conditions:

Sum(population) of all points/items/features in cluster 1=90,000
Sum(population) of all points/items/features in cluster 2=90,000
Sum(population) of all points/items/features in cluster 3=90,000

I tried to use the k-mean clustering algorithm which gave me 3 clusters, but how to force the above constraint (sum of population should equal 90,000)
Any idea is appreciated.