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: 4.1905499e-05
Overall Rank: 12,580 | 12.57%
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	0.00080846213
33	BIRCH: An Efficient Data Clustering Method for Very Large Databases	1996	SIGMOD	0.00077399244
277	Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications	1998	SIGMOD	0.00029356968
340	CURE: An Efficient Clustering Algorithm for Large Databases	1998	SIGMOD	0.00026854084
1,596	Fast Algorithms for Projected Clustering	1999	SIGMOD	0.00011210688
1,802	Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces	2000	VLDB	0.0001048177
2,023	Finding Generalized Projected Clusters in High Dimensional Spaces	2000	SIGMOD	9.7586371e-05
2,099	What is the nearest neighbor in high dimensional spaces?	2000	VLDB	9.5429949e-05
3,381	A Monte Carlo Algorithm for Fast Projective Clustering	2002	SIGMOD	7.1538392e-05
3,477	Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering	1999	VLDB	7.0558203e-05

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