Bayesian K-means as a "maximization-expectation" algorithm

Proceedings of the Sixth SIAM International Conference on Data Mining

Volumn 2006, Issue , 2006, Pages 474-478

  Welling, Max ^a   Kurihara, Kenichi ^b  

^a Irvine   (United States)

^b TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; COMPUTER SIMULATION; DATA PROCESSING; DATA STRUCTURES; OPTIMIZATION; PARAMETER ESTIMATION;

EM ALGORITHM; MAXIMIZATION PHASE; MODEL STRUCTURE; VARIABLES;

DATA MINING;

EID: 33745432181     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972764.43     Document Type: Conference Paper

References (11)

1. H. Attias. A variational bayesian framework for graphical models. In Neural Information Processing Systems 12, 2000.
2. David Eppstein. Fast hierarchical clustering and other applications of dynamic closest pairs. In SODA: ACM-SIAM Symposium on Discrete Algorithms, 1998.
3. Z. Ghahramani and M. J. Beal. Variational inference for Bayesian mixtures of factor analysers. In Neural Information Processing Systems, volume 12, 2000.
4. G. Hamerly and C. Elkan. Learning the k in k-means. In Neural Information Processing Systems, volume 17, 2003.
5. K. Heller and Z. Ghahramani. Bayesian hierarchical clustering. In Twenty-second International Conference on Machine Learning, 2005.
6. A. Moore. Very fast EM-based mixture model clustering using multiresolution kd-trees. In Neural Information Processing Systems Conference, 1998.
7. D. Pelleg and A. Moore. Accelerating exact k-means algorithms with geometric reasoning. In Proc. of the 5th Int'l Conf. on Knowledge Discovery in Databases, pages 277-281, 1999.
8. D. Pelleg and A. Moore. X-means: Extending K-means with efficient estimation of the number of clusters. In ICML, volume 17, pages 727-734, 2000.
9. N. Ueda, R. Nakano, Z. Ghahramani, and G.E. Hinton. SMEM algorithm for mixture models. Neural Computation, 12(9):2109-2128, 2000.
10. J. Verbeek, J. Nunnink, and N. Vlassis. Accelerated variants of the em algorithm for gaussian mixtures. Technical report, University of Amsterdam, 2003.
11. T. Zhang, R. Ramakrishnan, and M. Livny. Birch: An efficient data clustering method for very large databases. In ACM-SIGMOD Int. Conf. Management of Data, pages 103-114, 1996.