arxivst stuff from arxiv that you should probably bookmark

An Online Hierarchical Algorithm for Extreme Clustering

Abstract · Apr 6, 2017 14:29 ·

cs-lg stat-ml

Arxiv Abstract

  • Ari Kobren
  • Nicholas Monath
  • Akshay Krishnamurthy
  • Andrew McCallum

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.

Read the paper (pdf) »