Scalable Clustering Over High Dimensional Vector Streams

Summary: Suffice: clustering high-dimensional vector streams under value/vector/dimension updates with a streaming-native cluster definition. Key idea is adaptive multi-reference clusters with safe regions plus a double-hash index for nearest/furthest-neighbor search, cutting similarity checks and yielding 18–120× speedups. (summarized by gpt-5-mini on Apr 11 2026)

Paper ID: 7711
Venue: SIGMOD
Year: 2026
Pagerank: 5.093636e-05
Overall Rank: 10,497 | 27.99%
DOI: 10.1145/3786694

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Authors

1. Han Han (University of Arizona)
2. Beichuan Zhang (University of Arizona)
3. Lei Cao (Massachusetts Institute of Technology; University of Arizona)

BibTeX Citation

@inproceedings{han_sigmod26,
        title = {{Scalable Clustering Over High Dimensional Vector Streams}},
        author = {Han, Han and Zhang, Beichuan and Cao, Lei},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3786694},
        url = {https://dl.acm.org/doi/10.1145/3786694},
        year = {2026}
}

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Rank	Citing Paper	Year	Venue	Pagerank

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

Rank	Cited Paper	Year	Venue	Pagerank
2,580	Community Detection in Social Networks: An In-depth Benchmarking Study with a Procedure-Oriented Framework	2015	VLDB	8.3887788e-05

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