Database Paper Browser

Back to papers

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
10506
Venue
VLDB
Year
2012
Pagerank
9.5588104e-05
Overall Rank
2,093 | 85.45%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 13 of 13 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

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 0.00077324389
341 CURE: An Efficient Clustering Algorithm for Large Databases 1998 SIGMOD 0.00026810548
644 Densest Subgraph in Streaming and MapReduce 2012 VLDB 0.00018748988
886 Fast Personalized PageRank on MapReduce 2011 SIGMOD 0.00015597161
Previous Page 1 / 1 Next

Semantically Similar Papers