Data Bubbles for Non-Vector Data: Speeding-up Hierarchical Clustering in Arbitrary Metric Spaces

Summary: Data Bubbles, a distance-based summarization for non-vector data, speeds up hierarchical clustering in arbitrary metric spaces. By relying solely on pairwise distances and avoiding vector-space statistics, it yields compact representatives that preserve clustering structure with little quality loss and large runtime gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9187
Venue: VLDB
Year: 2003
Pagerank: 5.093636e-05
Overall Rank: 12,816 | 12.08%
DOI: 10.1016/B978-012722442-8/50047-1

Incoming Non-self Citations Over Time

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Authors

1. Jianjun Zhou (University of Alberta)
2. Jörg Sander (University of Alberta)

BibTeX Citation

@article{zhou_vldb03,
        title = {{Data Bubbles for Non-Vector Data: Speeding-up Hierarchical Clustering in Arbitrary Metric Spaces}},
        author = {Zhou, Jianjun and Sander, Jörg},
        journal = {PVLDB},
        series = {{VLDB} '03},
        doi = {10.1016/B978-012722442-8/50047-1},
        url = {https://doi.org/10.1016/B978-012722442-8/50047-1},
        year = {2003}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

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
31	BIRCH: An Efficient Data Clustering Method for Very Large Databases	1996	SIGMOD	0.00050347119
291	OPTICS: Ordering Points To Identify the Clustering Structure	1999	SIGMOD	0.00022264197
483	FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets	1995	SIGMOD	0.00017756569
9,013	Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering	2001	SIGMOD	5.3321448e-05

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