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Scalable K-Means++

Summary: Introduces k-means||, a parallel init for k-means++ that reduces startup passes to a logarithmic number. Proves near-optimality after O(log k) rounds; in practice a constant number of passes suffices, with experiments showing k-means|| outperforms k-means++ in both sequential and parallel modes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10507
Venue
VLDB
Year
2012
Pagerank
9.4341455e-05
Overall Rank
2,146 | 85.09%
DOI
-

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Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
33 BIRCH: An Efficient Data Clustering Method for Very Large Databases 1996 SIGMOD 0.00077399244
340 CURE: An Efficient Clustering Algorithm for Large Databases 1998 SIGMOD 0.00026854084
645 Densest Subgraph in Streaming and MapReduce 2012 VLDB 0.00018727714
875 Fast Personalized PageRank on MapReduce 2011 SIGMOD 0.00015679931
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