K-means projective clustering

Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems

Volumn 23, Issue , 2004, Pages 155-165

  Agarwal, Pankaj K ^a   Mustafa, Nabil H ^a  

^a Duke University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; HIERARCHICAL SYSTEMS; INDEXING (OF INFORMATION); PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; PROBLEM SOLVING; SAMPLING;

PROJECTIVE CLUSTERING; TREND ANALYSIS;

DATA MINING;

EID: 3142682233     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1055558.1055581     Document Type: Conference Paper

References (20)

1. P. Agarwal and S. Har-Peled, Maintaining the approximate extent measures of moving points, Proc. 12th ACM-SIAM Sympos. Discrete Algorithms, 2001, pp. 148-157.
2. P. K. Agarwal, S. Har-Peled, N. Mustafa, and Y. Wang, Near-linear time approximation algorithms for curve simplification in two and three dimensions, Proc. of the 10th European Symposium on Algorithms, 2002, pp. 544-555.
3. C. Aggarwal and P. Yu, Finding generalized projected clusters in high dimensional spaces, Proc. ACM-SIGMOD Intl. Conf. Management of Data, 2000, pp. 70-81.
4. C. C. Aggarwal, C. M. Procopiuc, J. L. Wolf, P. S. Yu, and J. S. Park, Fast algorithms for projected clustering, Proc. ACM SIGMOD Intl. Conf. on Management of Data, 1999, pp. 61-72.
5. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, Proc. ACM-SIGMOD Intl. Conf. Management of Data, 1998, pp. 94-105.
6. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, When is "nearest neighbor" meaningful?, Lecture Notes in Computer Science, 1540 (1999), 217-235.
7. K. Chakrabarti and S. Mehrotra, Local dimensionality reduction: A new approach to indexing high dimensional spaces, Proc. of 26th Intl. Conf. on Very Large Data Bases, 2000, pp. 89-100.
8. Q. Du, V. Faber, and M. Gunzburger, Centroidal voronoi tessellations: Applications and algorithms, SIAM Review, 41 (1999), 637-676.
9. S. Guha, R. Rastogi, and K. Shim, CURE: an efficient clustering algorithm for large databases, Proc. ACM-SIGMOD Intl. Conf. Management of Data, 1998, pp. 73-84.
10. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001.
11. S. Har-Peled and K. Varadarajan, Approximate shape fitting via linearization, Proc. 42nd Annu. IEEE Sympos. Found. Comput. Sci., 2001, pp. 66-73.
12. R. M. Heiberger, Algorithm AS 127: Generation of random orthogonal matrices, Appl. Statist., 27 (1978), 199-206.
13. A. Hinneburg, C. C. Aggarwal, and D. A. Keim, What is the nearest neighbor in high dimensional spaces?, Proc. 26th Intl. Conf. Very Large DataBases, 2000, pp. 506-515.
14. A. Hinneburg and D. A. Keim, Optimal grid-clustering:towards breaking the curse of dimensionality in high-dimensional clustering, Proc. of 25th Intl. Conf. Very Large DataBases, 1999, pp. 506-517.
15. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, Prentice Hall, Englewood Cliffs, NJ, 1988.
16. W. Johnson and J. Lindenstrauss, Extensions of Lipschitz maps into a Hilbert space, Contemp. Math., 26 (1984), 189-206.
17. T. T. Jolliffe, Principal component analysis, Springer-Verlag, New York, 2002.
18. R. T. Ng and J. Han, Efficient and effective clustering methods for spatial data mining, Intl. Conf. Very Large DataBases, 1994, pp. 144-155.
19. M. Procopiuc, M. Jones, P. K. Agarwal, and T. M. Murali, A monte carlo algorithm for fast projective clustering, Proc. ACM-SIGMOD Intl. Conf. Management of Data, 2002, pp. 418-427.
20. T. Zhang, R. Ramakrishnan, and M. Livny, BIRCH: an efficient data clustering method for very large databases, Proc. ACM-SIGMOD Intl. Conf. Management of Data, 1996, pp. 103-114.