I have ~100 sets of samples with integer IDs. For example, 3 of them could be:
a = [0, 1, 3, 4, 6...] b = [1, 5, 9, 102...] c = [1, 7, 10, 42...]
I am looking to cluster/group together these sets such that within each cluster, all the elements have at least X% common IDs with each other, where X is an input parameter.
I was thinking about using Agglomerative Clustering with 1 - %X as the distance metric, but was unsure how to modify it to account for each clusters 'information' being the common set of IDs between sets within it. Any advice would be appreciated (including a different technique/algorithm clustering was just what came to mind)