I have a group of n sets for which I need to calculate a sort of "uniqueness" or "similarity" value. I've settled on the Jaccard index as a suitable metric. Unfortunately, the Jaccard index only operates on two sets at a time. In order to calculate the similarity between all $n$ sets, it will require in the order of $n^2$ Jaccard calculations.
(If it helps, $n$ is usually between 10 and 10000, and each set contains on average 500 elements. Also, in the end, I don't care how similar any two specific sets are - rather, I only care what the internal similarity of the whole group of sets is. (In other words, the mean (or at least a sufficiently accurate approximation of the mean) of all Jaccard indexes in the group))
- Is there a way to still use the Jaccard index without the $n^2$ complexity?
- Is there a better way to calculate set similarity/uniqueness across a group of sets than the way I've suggested above?