Data Stream Clustering: An In-depth Empirical Study

Summary: Empirical DSC study across four design axes: data summarization, windowing, outlier detection, and offline refinement; implemented from scratch and tested on real and synthetic streams. Introduces Benne, a tunable hybrid that can boost accuracy or efficiency by mixing design choices. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6665
Venue: SIGMOD
Year: 2023
Pagerank: 4.7180617e-05
Overall Rank: 7,488 | 47.91%
DOI: 10.1145/3589307

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,718	BURST: Rendering Clustering Techniques Suitable for Evolving Streams	2025	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 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
662	A Framework for Clustering Evolving Data Streams	2003	VLDB	0.00018475968
5,324	Clustering Stream Data by Exploring the Evolution of Density Mountain	2018	VLDB	5.5691645e-05

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5,324	Clustering Stream Data by Exploring the Evolution of Density Mountain	2018	VLDB	5.5691645e-05