Scaling log-linear analysis to high-dimensional data

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2013, Pages 597-606

  Petitjean, François ^a   Webb, Geoffrey I ^a   Nicholson, Ann E ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

Association Discovery; Data Modeling; High dimensional Data; Log linear Analysis

Indexed keywords

EID: 84894670904     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2013.17     Document Type: Conference Paper

References (24)

1. S. J. Haberman, The analysis of frequency data. University of Chicago Press, 1974.
2. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules in large databases," in Int. Conf. on Very Large Data Bases, 1994, pp. 487-499.
3. X. Wu, D. Barbará, and Y. Ye, "Screening and interpreting multi-item associations based on log-linear modeling," in Int. Conf. on Knowledge Discovery and Data Mining, 2003, pp. 276-285.
4. S.-H. Kim and S. Lee, "Searching model structures based on marginal model structures," in New Developments in Robotics Automation and Control. InTech, 2008, pp. 355-376.
5. C. Dahinden, M. Kalisch, and P. Bühlmann, "Decomposition and model selection for large contingency tables," Biometrical Journal, vol. 52, no. 2, pp. 233-252, 2010.
6. 1-regularized logistic regression," Annals of Statistics, vol. 38, no. 3, pp. 1287-1319, 2010.
7. R. Christensen, Log-Linear Models and Logistic Regression Second Edition. Springer, 1997.
8. S. E. Fienberg and A. Rinaldo, "Maximum likelihood estimation in log-linear models," The Annals of Statistics, vol. 40, no. 2, pp. 996-1023, 2012.
9. P. Heggernes, "Minimal triangulations of graphs: A survey," Discrete Mathematics, vol. 306, no. 3, pp. 297-317, 2006.
10. M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, "New algorithms for fast discovery of association rules," in Int. Conf. on Knowledge Discovery and Data Mining, 1997, pp. 283-286.
11. G. I. Webb, "Layered critical values: a powerful directadjustment approach to discovering significant patterns," Machine Learning, vol. 71, no. 2-3, pp. 307-323, 2008.
12. A. Berry, A. Sigayret, and C. Sinoquet, "Maximal subtriangulation in pre-processing phylogenetic data," Soft Computing, vol. 10, no. 5, pp. 461-468, 2006.
13. F. Malvestuto, "Approximating discrete probability distributions with decomposable models," IEEE Transactions on Systems, Man and Cybernetics, vol. 21, no. 5, pp. 1287-1294, 1991.
14. A. Deshpande, M. Garofalakis, and M. I. Jordan, "Efficient stepwise selection in decomposable models," in Uncertainty in Artificial Intelligence, 2001, pp. 128-135.
15. M. J. Zaki and K. Gouda, "Fast vertical mining using diffsets," in Int. Conf. on Knowledge Discovery and Data Mining. ACM, 2003, pp. 326-335.
16. V. Gogate, W. A. Webb, and P. Domingos, "Learning Efficient Markov Networks," in Advances in Neural Information Processing Systems 23, 2010, pp. 748-756.
17. 1-Regularization," in Advances in neural Information processing systems 18, 2006, pp. 817-824.
18. A. Deshpande, M. Garofalakis, and R. Rastogi, "Independence is good: dependency-based histogram synopses for high-dimensional data," in Int. Conf. on Management of Data, 2001, pp. 199-210.
19. J. O. Taylor, R. B. Wallace, A. M. Ostfeld, and D. G. Blazer, "Established Populations for Epidemiologic Studies of the Elderly, 1981-1993," Ann Arbor, MI: Inter-university Consortium for Political and Social Research, 1998, http://dx.doi.org/10.3886/ICPSR09915.
20. J. Flores, A. E. Nicholson, A. Brunskill, K. B. Korb, and S. Mascaro, "Incorporating expert knowledge when learning Bayesian network structure: A medical case study," Artificial Intelligence in Medicine, vol. 53, no. 3, pp. 181-204, 2011.
21. C. Reitz, T. den Heijer, C. van Duijn, A. Hofman, and M. Breteler, "Relation between smoking and risk of dementia and alzheimer disease: The Rotterdam study," Neurology, vol. 69, no. 10, pp. 998-1005, 2007.
22. W. Lam and F. Bacchus, "Learning Bayesian belief networks: An approach based on the MDL principle," Computational Intelligence, vol. 10, pp. 269-293, 1993.
23. C. S. Wallace, K. Korb, and H. Dai, "Causal discovery via MML," in Int. Conf. on Machine Learning, 1996, pp. 516-524.
24. A. Berry and R. Pogorelcnik, "A simple algorithm to generate the minimal separators and the maximal cliques of a chordal graph," Information Processing Letters, vol. 111, no. 11, pp. 508-511, 2011.