Por Luis Carvalho (Boston University, Massachusetts).

We discuss a novel approach for modeling multivariate binary transaction data and inferring co-purchase patterns in market basket data. To this end, we exploit a latent graph capturing these purchase associations, where each transaction is a clique, and set meaningful priors based on expected transaction sizes and frequency. We present a MCMC sampling procedure that handles large datasets and conclude that this model provides sparser representations of inferred associations compared to traditional frequent itemset mining (FIM) approaches, without sacrificing predictive accuracy. This is joint work with David Reynolds.

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