I have developed an iterative process via which I can collect data in batches. The data are points in a predefined 3D space. I am trying to explore and locate clusters in that 3D space based on my data. After some batches have been collected I can locate and create the clusters. As I collect even more data though, small refinements are being made and the new results are not really worth the effort. How can determine whether a new batch is "worth" collecting? Is there some metric I can use to measure how much the batches I have are "similar" or whether a new random batch will affect the overall process?