Towards Association Rules with Hidden Variables

  • Ricardo Silva
  • Richard Scheines
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


The mining of association rules can provide relevant and novel information to the data analyst. However, current techniques do not take into account that the observed associations may arise from variables that are unrecorded in the database. For instance, the pattern of answers in a large marketing survey might be better explained by a few latent traits of the population than by direct association among measured items. Techniques for mining association rules with hidden variables are still largely unexplored. This paper provides a sound methodology for finding association rules of the type HA 1, ..., A k , where H is a hidden variable inferred to exist by making suitable assumptions and A 1, ..., A k are discrete binary or ordinal variables in the database.


Association Rule Latent Trait Hide Variable Association Rule Mining Latent Variable Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bartholomew, D., Steele, F., Moustaki, I., Galbraith, J.: The Analysis and Interpretation of Multivariate Data for Social Scientists. Arnold Publishers (2002)Google Scholar
  2. 2.
    Borgelt, C., Kruse, R.: Induction of association rules: Apriori implementation. In: 15th Conference on Computational Statistics (2002)Google Scholar
  3. 3.
    Buntine, W., Jakulin, A.: Applying discrete PCA in data analysis. In: Proceedings of 20th Conference on Uncertainty in Artificial Intelligence (2004)Google Scholar
  4. 4.
    Gibson, J.: Freedom and Tolerance in the United States. Chicago, IL: University of Chicago, National Opinion Research Center [producer] (1987). Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor] (1991)Google Scholar
  5. 5.
    Silva, R.: Automatic discovery of latent variable models. PhD Thesis, Machine Learning Department, Carnegie Mellon University (2005)Google Scholar
  6. 6.
    Silva, R., Scheines, R., Glymour, C., Spirtes, P.: Learning the structure of linear latent variable models. Journal of Machine Learning Research 7, 191–246 (2006)MathSciNetGoogle Scholar
  7. 7.
    Silverstein, C., Brin, S., Motwani, R., Ullman, J.: Scalable techniques for mining causal structures. Data Mining and Knowledge Discovery (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ricardo Silva
    • 1
  • Richard Scheines
    • 2
  1. 1.Gatsby UnitUniversity College LondonLondonUK
  2. 2.Machine Learning Department, Carnegie MellonPittsburghUSA

Personalised recommendations