k-Means Projective Clustering

Summary: Propose a new projective-clustering objective that explicitly trades off subspace dimension vs clustering/reconstruction error, enabling automatic per-cluster dimension selection. Extend k-means to arbitrary subspaces with local-minima-avoidance heuristics; empirically outperforms prior methods. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 1318
Venue: PODS
Year: 2004
Pagerank: 4.1945683e-05
Overall Rank: 12,571 | 12.55%
DOI: -

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Authors

1. Pankaj K. Agarwal
2. Nabil H. Mustafa

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
27	Efficient and Effective Clustering Methods for Spatial Data Mining	1994	VLDB	0.00080736878
33	BIRCH: An Efficient Data Clustering Method for Very Large Databases	1996	SIGMOD	0.00077324389
277	Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications	1998	SIGMOD	0.00029311426
341	CURE: An Efficient Clustering Algorithm for Large Databases	1998	SIGMOD	0.00026810548
1,595	Fast Algorithms for Projected Clustering	1999	SIGMOD	0.00011222442
1,806	Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces	2000	VLDB	0.00010490769
2,019	Finding Generalized Projected Clusters in High Dimensional Spaces	2000	SIGMOD	9.7707059e-05
2,107	What is the nearest neighbor in high dimensional spaces?	2000	VLDB	9.5330494e-05
3,376	A Monte Carlo Algorithm for Fast Projective Clustering	2002	SIGMOD	7.1630476e-05
3,475	Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering	1999	VLDB	7.0614822e-05

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