An accelerated algorithm for density estimation in large databases using Gaussian mixtures

Cybernetics and Systems

Volumn 38, Issue 2, 2007, Pages 123-139

  Soto, Alvaro ^a   Zavala, Felipe ^a   Araneda, Anita ^a  

^a PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE   (Chile)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA STORAGE EQUIPMENT; OPTIMIZATION; PARAMETER ESTIMATION; PROBLEM SOLVING;

COMPUTER STORAGE; DATASETS; DENSITY ESTIMATION; GAUSSIAN MIXTURE MODELS (GMM);

DATABASE SYSTEMS;

EID: 33847355183     PISSN: 01969722     EISSN: 10876553     Source Type: Journal    
DOI: 10.1080/01969720601138928     Document Type: Article

References (20)

1. Bentley, J. L. 1975. Multidimensional binary search trees used for associative searching. Communication of the ACM, 18:509-517.
2. Bradley, P., U. Fayyad, and C. Reina. 1998. Scaling EM (expectation maximization) clustering to large databases. Technical Report MSR-TR-98-35, Microsoft Research.
3. Cormen, T., C. Leiserson, R. Rivest, and C. Stein. 1990. Introduction to algorithms. Cambridge, MA: MIT Press.
4. Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39:1-38.
5. Domingos, P. and G. Hulten. 2002. Learning from infinite data in finite time. In Advances in neural information processing systems 14, edited by T. G Dietterich, S. Becker, and Z. Ghahramani. Cambridge, MA: MIT Press.
6. Duda, R. O. and P. E. Hart. 1973. Pattern classification and scene analysis. New York: John Wiley and Sons.
7. Hamerly, G. and C. Elkan. 2004. Learning the k in k-means. In Advances in neural information processing systems 16, edited by S. Thrun, L. Saul, and B. Schölkopf. Cambridge, MA: MIT Press.
8. Hu, X. and L. Xu. 2004. Investigation on several model selection criteria for determining the number of cluster. Neural Information Processing - Letters and Reviews, 4(1): 1-10.
9. Mitchell, T. 1997. Machine learning. New York, NY: McGraw Hill.
10. Moore, A. 1999. Very fast EM-based mixture model clustering using multiresolution KD-trees. In Advances in neural information processing systems, edited by M. Kearns and D. Cohn. San Francisco, CA: Morgan Kaufman, pp. 543-549.
11. Moore, A. W. 2000. The anchors hierarchy: Using the triangle inequality to survive high-dimensional data. In Proceedings of the 16th conference in uncertainty in artificial intelligence, edited by C. Boutilier and M. Goldszmidt. San Francisco, CA: Morgan Kaufman, pp. 397-405.
12. Neal, R. and G. Hinton. 1998. A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in graphical models, edited by M. Jordan. Dordetch, The Netherlands: Kluwer Academic Publishers, pp. 355-368.
13. Omohundro, S. M. 1989. Five balltree construction algorithms. Technical Report TR-89-063, International Computer Science Institute.
14. Omohundro, S. M. 1991. Bumptrees for efficient function, constraint, and classification learning. In Advances in neural information processing systems 3, Proc. Of the third NIPS Conference, edited by R. L. Lippmann, J. E. Moody, and D. S. Touretzky. Denver, Colorado: Morgan Kauffmann, pp. 693-699.
15. Pelleg, D. and A. Moore. 2000. X-means: Extending k-means with efficient estimation of the number of clusters. In Proceedings of the seventeenth nternational conference on machine learning, edited by P. Langley. San Francisco, CA: Morgan Kaufmann, pp. 727-734.
16. Poland, W. B. and R. D. Shachter. 1994. Three approaches to probability model selection. In Uncertainty in artificial intelligence: Proceedings of the tenth conference, edited by R. L. de Mántaras and D. Poole. San Francisco, CA: Morgan Kaufmann, pp. 478-483.
17. Rissanen, J. 1978. Modeling by shortest data description. Automatica, 14: 465-471.
18. Sand. P. and A. Moore. 2001. Repairing faulty mixture models using density estimation. In International conference on machine learning, edited by C. E. Brodley and A. P. Danyluk. San Francisco, CA: Morgan Kaufmann pp. 457-464.
19. Schervish, M. 1995. Theory of Statistics. New York, NY: Springer-Verlag.
20. Zhang, T., R. Ramakrishnan, and M. Livny. 1996. BIRCH: An efficient data clustering method for very large databases. In SIGMOD '96: Proceedings of the 1996 Acm SIGMOD international conference on management of data, edited by H. V. Jagadish and I. S. Mumick. New York, NY: ACM Press, pp. 103-114.