Constrained Locally Weighted Clustering

Summary: Constrained Locally Weighted Clustering learns a per-cluster weighting vector in an adaptive subspace to capture local correlations. It uses pairwise constraints to group constrained points and assign groups to feasible clusters by minimizing group-centroid distances; theory and experiments show superior accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9660
Venue: VLDB
Year: 2008
Pagerank: 4.1945683e-05
Overall Rank: 12,379 | 13.89%
DOI: -

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Rank	Citing Paper	Year	Venue	Pagerank

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

Rank	Cited Paper	Year	Venue	Pagerank
1,595	Fast Algorithms for Projected Clustering	1999	SIGMOD	0.00011222442
2,019	Finding Generalized Projected Clusters in High Dimensional Spaces	2000	SIGMOD	9.7707059e-05
8,647	A Non-Linear Dimensionality-Reduction Technique for Fast Similarity Search in Large Databases	2006	SIGMOD	4.4768766e-05

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