Accelerating EM for large databases

Machine Learning

Volumn 45, Issue 3, 2001, Pages 279-299

  Thiesson, Bo ^a   Meek, Christopher ^a   Heckerman, David ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

Clustering; Data blocking; Expectation Maximization algorithm; Incremental EM; Lazy EM; Mixture models; Online EM

Indexed keywords

ALGORITHMS; COMPUTATION THEORY; DATA REDUCTION; OPTIMIZATION; PARAMETER ESTIMATION;

EXPECTATION MAXIMIZATION (EM) ALGORITHMS;

DATABASE SYSTEMS;

EID: 0035575419     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1017986506241     Document Type: Article

References (22)

1. Scaling EM (Expectation Maximization) clustering to large databases
2. Bayesian classification (AutoClass): Theory and results
3. Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables
4. Fast learning from sparse data
5. Maximum likelihood from incomplete data via the EM algorithm (with discussion)
6. On use of the EM algorithm for penalized likelihood estimation
7. Microsoft Windows highly intelligent speech recognizer: Whisper
8. Conjugate gradient acceleration of the EM algorithm
9. Finding the observed information matrix when using the EM algorithm
10. Efficient clustering of high-dimensional data sets with application to reference matching
11. A fast improvement to the EM algorithm on its own terms
12. Maximum likelihood estimation via the ECM algorithm: A general framework
13. The EM algorithm - An old folksong sung to a fast new tune (with discussion)
14. Very fast EM-based mixture model clustering using multiresolution kd-trees
15. A view of the EM algorithm that justifies incremental, sparse, and other variants
16. On-line EM algorithm for the normalized Gaussian network
17. Accelerated quantification of Bayesian networks with incomplete data
18. Computational efficient methods for selecting among mixtures of graphical models, with discussion
19. Birch: An efficient data clustering method for very large databases