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: 6727
Venue: SIGMOD
Year: 2023
Pagerank: 5.5181056e-05
Overall Rank: 7,912 | 45.72%
DOI: 10.1145/3589307

Incoming Non-self Citations Over Time

Authors

1. Xin Wang (Ohio State University; Sichuan University)
2. Zhengru Wang (Huazhong University of Science and Technology; NVIDIA)
3. Zhenyu Wu (University of Manchester)
4. Shuhao Zhang (Singapore Institute of Technology)
5. Xuanhua Shi (Huazhong University of Science and Technology)
6. Li Lu (Sichuan University)

BibTeX Citation

@inproceedings{wang_sigmod23,
        title = {{Data Stream Clustering: An In-depth Empirical Study}},
        author = {Wang, Xin and Wang, Zhengru and Wu, Zhenyu and Zhang, Shuhao and Shi, Xuanhua and Lu, Li},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3589307},
        url = {https://dl.acm.org/doi/10.1145/3589307},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,961	BURST: Rendering Clustering Techniques Suitable for Evolving Streams	2025	VLDB	5.093636e-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
31	BIRCH: An Efficient Data Clustering Method for Very Large Databases	1996	SIGMOD	0.00050347119
907	A Framework for Clustering Evolving Data Streams	2003	VLDB	0.00013309819
5,335	Clustering Stream Data by Exploring the Evolution of Density Mountain	2018	VLDB	6.2618226e-05

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