Scalable Techniques for Mining Causal Structures
Data Mining and Knowledge Discovery
Applying discrete PCA in data analysis
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Automatic discovery of latent variable models
Automatic discovery of latent variable models
Learning the Structure of Linear Latent Variable Models
The Journal of Machine Learning Research
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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 H →A1, ..., Ak, where H is a hidden variable inferred to exist by making suitable assumptions and A1, ..., Ak are discrete binary or ordinal variables in the database.