Projective clustering in high dimensions using core-sets

Proceedings of the Annual Symposium on Computational Geometry

Volumn , Issue , 2002, Pages 312-318

  Har Peled, Sariel ^a   Varadarajan, Kasturi ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

Computational convexity; Projective clustering

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL COMPLEXITY; SET THEORY;

PROJECTIVE CLUSTERING;

COMPUTER SCIENCE;

EID: 0036367320     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/513400.513440     Document Type: Conference Paper

References (21)

1. Database-friendly random projections
2. Testing of clustering
3. Approximation algorithms for projective clustering
4. Computing the volume is difficult
5. Approximation algorithms for geometric problems
6. Efficiently approximating the minimum-volume bounding box of a point set in three dimensions
7. Approximate clustering via core-sets
8. Inapproximability of some geometric and quadratic optimization problems
9. Inner and outer j-radii of convex bodies in finite-dimensional normed spaces
10. Computational complexity of inner and outer j-radii of polytopes in finite-dimensional normed spaces
11. On the complexity of some basic problems in computational convexity: I. containment problems
12. Geometric algorithms and combinatorial optimization
13. Clustering motion
14. Approximate shape fitting via linearization
15. Algorithmic applications of low-distortion geometric embeddings
16. Extensions of lipshitz mapping into hilbert space
17. Sublinear time approximate clustering
18. On the complexity of locating linear facilities in the plane