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: 9851
Venue: VLDB
Year: 2008
Pagerank: 5.093636e-05
Overall Rank: 12,572 | 13.75%
DOI: 10.14778/1453856.1453871

Incoming Non-self Citations Over Time

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Authors

1. Hao Cheng (University of Central Florida)
2. Kien A. Hua (University of Central Florida)
3. Khanh Vu (University of Central Florida)

BibTeX Citation

@article{cheng_vldb08,
        title = {{Constrained Locally Weighted Clustering}},
        author = {Cheng, Hao and Hua, Kien A. and Vu, Khanh},
        journal = {PVLDB},
        series = {{VLDB} '08},
        pages = {90},
        doi = {10.14778/1453856.1453871},
        url = {https://doi.org/10.14778/1453856.1453871},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

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
1,646	Fast Algorithms for Projected Clustering	1999	SIGMOD	0.00010128564
1,833	Finding Generalized Projected Clusters in High Dimensional Spaces	2000	SIGMOD	9.6566846e-05
8,655	A Non-Linear Dimensionality-Reduction Technique for Fast Similarity Search in Large Databases	2006	SIGMOD	5.3910353e-05

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