Title: An Online Hierarchical Algorithm for Extreme Clustering,
Abstract: Many modern clustering methods scale well to a large number of data items, N,
but not to a large number of clusters, K. This paper introduces PERCH, a new
non-greedy algorithm for online 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 online 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.