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: h91e048cc383ffa26
Venue: VLDB
Year: 2008
Pagerank: 4.9793485e-05
Overall Rank: 12,862 | 13.53%
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
1,679	Fast Algorithms for Projected Clustering	1999	SIGMOD	9.9079414e-05
1,881	Finding Generalized Projected Clusters in High Dimensional Spaces	2000	SIGMOD	9.4403736e-05
8,818	A Non-Linear Dimensionality-Reduction Technique for Fast Similarity Search in Large Databases	2006	SIGMOD	5.2700772e-05

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