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• W2744293559 abstract "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." @default.
• W2744293559 created "2017-08-17" @default.
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• W2744293559 date "2017-08-04" @default.
• W2744293559 modified "2023-10-17" @default.
• W2744293559 title "A Hierarchical Algorithm for Extreme Clustering" @default.
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• W2744293559 doi "https://doi.org/10.1145/3097983.3098079" @default.
• W2744293559 hasPublicationYear "2017" @default.
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