Papers › Scalable k-Means Clustering for Large k via Seeded Approximate Nearest-Neighbor Search
Scalable k-Means Clustering for Large k via Seeded Approximate Nearest-Neighbor Search
Jack Spalding-Jamieson, Eliot Wong Robson, Da Wei Zheng
For very large values of k, we consider methods for fast k-means clustering of massive datasets with 10⁷∼10⁹ points in high-dimensions (d≥100). All current practical methods for this problem have runtimes at least Ω(k²). We find that initialization routines are not a bottleneck for this case. Instead, it is critical to improve the speed of Lloyd's local-search algorithm, particularly the step that reassigns points to their closest center. Attempting to improve this step naturally leads us to leverage approximate nearest-neighbor search methods, although this alone is not enough to be practical. Instead, we propose a family of problems we call "Seeded Approximate Nearest-Neighbor Search", for which we propose "Seeded Search-Graph" methods as a solution.
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