Scalable Hierarchical Clustering via Tree Grafting
Nicholas Monath (University of Massachusetts Amherst);Ari Kobren (UMass Amherst);Akshay Krishnamurthy (University of Massachusetts, Amherst);Michael Glass (IBM);Andrew Mccallum (University of Massachusetts);
We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets. The key components of Grinch are its rotate and graft subroutines that efficiently reconfigure the hierarchy as new points arrive, supporting discovery of clusters with complex structure. Grinch is motivated by a new notion of separability for clustering with linkage functions: we prove that when the linkage function is consistent with a ground-truth clustering, Grinch is guaranteed to produce a cluster tree containing the ground-truth, independent of data arrival order. Our empirical results on benchmark and author coreference datasets (with standard and learned linkage functions) show that Grinch is more accurate than other scalable methods, and orders of magnitude faster than hierarchical agglomerative clustering.
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