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Motivated by the problem of accurately predicting gap times betweensuccessive blood donations, we present here a general class of Bayesiannonparametric models for clustering. These models allow for prediction of newrecurrences, accommodating covariate information that describes the personalcharacteristics of the sample individuals. We introduce a prior for the randompartition of the sample individuals which encourages two individuals to beco-clustered if they have similar covariate values. Our prior generalizes PPMxmodels in the literature, which are defined in terms of cohesion and similarityfunctions. We assume cohesion functions which yield mixtures of PPMx models,while our similarity functions represent the compactness of a cluster. We showthat including covariate information in the prior specification improves theposterior predictive performance and helps interpret the estimated clusters, interms of covariates in the blood donation application.

Clustering blood donors via mixtures of product partition models with covariates