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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
1023
Venue
PODS
Year
1994
Pagerank
6.2167147e-05
Overall Rank
4,404 | 69.37%
DOI
-

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Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
285 Automating Physical Database Design in a Parallel Database 2002 SIGMOD 0.0002899128
6,076 Window Query-Optimal Clustering of Spatial Objects 1995 PODS 5.223696e-05
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