Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering

Summary: Introduces Data Bubbles, a compression-based pipeline to scale OPTICS: compress to representatives, cluster the compressed data, then infer the full clustering. Tackles three failure modes of naive sampling/BIRCH via post-processing and the Data Bubble concept, enabling near-accurate clustering at high compression with minimal quality loss. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 3259
Venue: SIGMOD
Year: 2001
Pagerank: 4.438882e-05
Overall Rank: 8,838 | 38.52%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,799	Incremental and Effective Data Summarization for Dynamic Hierarchical Clustering	2004	SIGMOD	4.9232394e-05
6,883	C2P: Clustering based on Closest Pairs	2001	VLDB	4.8960306e-05
12,623	Data Bubbles for Non-Vector Data: Speeding-up Hierarchical Clustering in Arbitrary Metric Spaces	2003	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 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

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