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A Decomposition-Based Simulated Annealing Technique for Data Clustering

Summary: Recasts data clustering as a graph-partitioning problem and introduces a decomposition-based simulated annealing that randomly samples subgraphs into memory to avoid poor locality and thrashing. Dramatically cuts disk I/O while preserving high-quality clustering results. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1022
Venue
PODS
Year
1994
Pagerank
6.4032846e-05
Overall Rank
5,007 | 65.65%
DOI
10.1145/182591.182605

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{hua_pods94,
        address = {New York, NY, USA},
        series = {{PODS} '94},
        title = {{A Decomposition-Based Simulated Annealing Technique for Data Clustering}},
        url = {https://dl.acm.org/doi/10.1145/182591.182605},
        doi = {10.1145/182591.182605},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Hua, Kien A. and Lang, S. D. and Lee, Wen K.},
        year = {1994}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
246 Automating Physical Database Design in a Parallel Database 2002 SIGMOD 0.00023457421
6,089 Window Query-Optimal Clustering of Spatial Objects 1995 PODS 5.9806097e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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