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  • $\begingroup$ Your proposed procedure needs some clarification. What do you mean by "stop clustering"? Some algorithms iteratively cluster and re-cluster the entire dataset, whereas other algorithms build clusters in batches, or one data point at a time. You will need to clarify this before the question can be answered. $\endgroup$ Commented Oct 4, 2018 at 13:01
  • $\begingroup$ I think I mean one data point at a time. I think I need an algorythm that starts with placing each point in a seperate cluster, and then continues to merge clusters untill that numerical threshold value is reached. Note, I said that's what I THINK needs to happen. Maybe there are other algorythms that do work iteratively but give me the same result. $\endgroup$ Commented Oct 4, 2018 at 13:42
  • $\begingroup$ With of course taking into account the distance (the points need to be close to each other), and they also need to belong to the same category(type) $\endgroup$ Commented Oct 4, 2018 at 13:43
  • $\begingroup$ Is it only important to add the closest points to a cluster while a cluster still has capacity, or is it also important to capture your numerical value efficiently (so that your cluster preferentially chooses the highest value points as in a knapsack problem)? $\endgroup$ Commented Sep 5, 2020 at 6:48
  • $\begingroup$ Also, must all your points belong to a cluster? $\endgroup$ Commented Sep 5, 2020 at 6:49