Association models for web mining

Data Mining and Knowledge Discovery

Volumn 5, Issue 3, 2001, Pages 183-196

  Giudici, Paolo ^a   Castelo, Robert ^b  

^a UNIVERSITY OF PAVIA   (Italy)

^b UTRECHT UNIVERSITY   (Netherlands)

Author keywords

Bayesian inference; Data mining; Graphical models; Markov chain Monte Carlo methods; Model selection

Indexed keywords

EID: 23044528417     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1011469000311     Document Type: Article

References (9)

1. Breese, J., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. New York: Morgan Kaufmann.
2. Brooks, S. 1998. Markov chain Monte Carlo method and its application. The Statistician, 47:69-100.
3. Giudici, P. and Castelo, R. 2001. Improving Markov chain Monte Carlo, for data mining. Manuscript revised for publication.
4. Giudici, P. and Green, P. 1999. Decomposable graphical Gaussian model determination. Biometrika, 86:785-801.
5. Heckerman, D., Chickering, D., Meek, C., Rounthwaite, R., and Kadie, C. 2000. Dependency networks for inference, collaborative filtering and data visualization. Journal of Machine Learning Research, 1:49-75.
6. Heckerman, D., Geiger, D., and Chikering, D. 1995. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197-243.
7. Lauritzen, S. 1996. Graphical Models. Oxford: Oxford University Press.
8. Madigan, D. and York, J. 1995. Bayesian graphical models for discrete data. International Statistical Review, 63:215-232.
9. Whittaker, J. 1990. Graphical Models in Applied Multivariate Statistics. New York: Wiley.