KMeans: Extracting the parameters/rules that fill up the clusters
Got the answer in a different topic: 
Just record the cluster means. Then when new data comes in, compare it to each mean and put it in the one with the closest mean.
I have created a 4-cluster k-means customer segmentation in scikit learn (Python). The idea is that every month, the business gets an overview of the shifts in size of our customers in each cluster.
My question is how to make these clusters 'durable'. If I rerun my script with updated data, the 'boundaries' of the clusters may slightly shift, but I want to keep the old clusters (even though they fit the data slightly worse).
My guess is that there should be a way to extract the paramaters that decides which case goes to their respective cluster, but I haven't found the solution yet.
I would appreciate any help