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: 8996
Venue: VLDB
Year: 2003
Pagerank: 4.1945683e-05
Overall Rank: 12,623 | 12.19%
DOI: -

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Authors

1. Jianjun Zhou
2. Jörg Sander

Incoming Citations (Sorted by Pagerank)

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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
33	BIRCH: An Efficient Data Clustering Method for Very Large Databases	1996	SIGMOD	0.00077324389
270	OPTICS: Ordering Points To Identify the Clustering Structure	1999	SIGMOD	0.00029505642
471	FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets	1995	SIGMOD	0.00022364776
8,838	Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering	2001	SIGMOD	4.438882e-05

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