An Online Hierarchical Algorithm for Extreme Clustering
Ari Kobren (University of Massachusetts Amherst);Nicholas Monath (University of Massachusetts Amherst);Akshay Krishnamurthy (University of Massachusetts Amherst);Andrew McCallum (University of Massachusetts Amherst)
Many modern clustering methods scale well to a large number of data points, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy, incremental algorithm for hierarchical clustering that scales to both massive N and K—-a problem setting we term extreme clustering. Our algorithm efficiently routes new data points to the leaves of an incrementally-built tree. Motivated by the desire for both accuracy and speed, our approach performs tree rotations for the sake of enhancing subtree purity and encouraging balancedness. We prove that, under a natural separability assumption, our non-greedy algorithm will produce trees with perfect dendrogram purity regardless of data arrival order. Our experiments demonstrate that PERCH constructs more accurate trees than other tree-building clustering algorithms and scales well with both N and K, achieving a higher quality clustering than the strongest flat clustering competitor in nearly half the time.