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    <p>This is one of those (very common) situations where the more we know about your data and application the better we are able to help. In the absence of better information than you provide, and in the spirit of exposing the weakness of answers such as this in that absence, here are a couple of suggestions you might reject.</p> <p>One appropriate data structure for set operations is a bit-set, that is a set of length equal to the cardinality of the underlying universe of things in which each bit is set on or off according to the things membership of the (sub-set). You could implement this in Matlab in at least two ways:</p> <p>a) (easy, but possibly consuming too much space): define a matrix with as many columns as there are points in your data, and one row for each cluster. Set the (cluster, point) value to true if point is a member of cluster. Set operations are then defined by vector operations. I don't have a clue about the relative (time) efficiency of setdiff versus rowA==rowB.</p> <p>b) (more difficult): actually represent the clusters by bit sets. You'll have to use Matlab's bit-twiddling capabilities of course, but the pain might be worth the gain. Suppose that your universe comprises 1024 points, then you'll need an array of 16 uint64 values to represent the bit set for each cluster. The presence of, say, point 563 in a cluster requires that you set, for the bit set representing that cluster, bit 563 (which is probably bit 51 in the 9th element of the set) to 1.</p> <p>And perhaps I should have started by writing that I don't think that this is a hashing sort of a problem, it's a set sort of a problem. Yeah, you could use a hash but then you'll have to program around the limitations of using a screwdriver on a nail (choose your preferred analogy).</p>
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    1. USHigh Performance Mark
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    1. COThanks for the reply. But I still believe my problem is a hashing sort of a problem. Let me try to clarify. My data consists of 60K points in 3D. I am implementing a custom version of K-means clustering. Once I calculate the closest points to a cluster I calculate several properties of the cluster based on these points, statistical distributions and so on. This is computationally heavy. So in order to avoid doing that I want to know if I have the same points in my clusters as I did last iteration. Currently I am comparing the mean of the points 3D coordinates, searching for something faster.
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