 Latent association graph inference for binary transaction dataDavid Reynolds and Luis Carvalho Computational Statistics & Data Analysis, 2021, vol. 160, issue C Abstract: A novel approach to the problem of statistical inference for multivariate binary transaction data is proposed. A fundamental question that arises from this data, often referred to as market basket data, is how the items relate to one another. These relationships are naturally expressed by a graph and transactions can be modeled as samples of cliques from this association graph. A hierarchical model is developed that follows from this generative idea, along with an MCMC sampling procedure that handles large datasets and allows inference on a broad set of parameters. This model provides a sparser representation of associations between items as compared with frequent itemset mining (FIM) output, without sacrificing predictive accuracy. Additionally, by allowing inference on a broad set of parameters, the model provides a deeper level of insight into transaction data. Empirical results are provided on applications of this model to simulated data and real transaction data from Instacart. Keywords: Hierarchical model; Bayesian inference; Graph theory; Bayesian model averaging (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:160:y:2021:i:c:s0167947321000633 DOI: 10.1016/j.csda.2021.107229 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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