An enriched mixture model for functional clustering

There is an increasingly rich literature about Bayesian nonparametric models for clustering functional observations. Most recent proposals rely on infinite-dimensional characterizations that might lead to overly complex cluster solutions. In addition, while prior knowledge about the functional shapes is typically available, its practical exploitation might be a difficult modeling task. Motivated by an application in e-commerce, we propose a novel enriched Dirichlet mixture model for functional data. Our proposal accommodates the incorporation of functional constraints while bounding the model complexity. We characterize the prior process through a urn scheme to clarify the underlying partition mechanism. These features lead to a very interpretable clustering method compared to available techniques. Moreover, we employ a variational Bayes approximation for tractable posterior inference to overcome computational bottlenecks.

Automated summary

This paper introduces a novel enriched Dirichlet mixture model for clustering functional data, incorporating functional constraints and bounding the model complexity. The prior process is characterized through a urn scheme, enhancing the interpretability of clustering. Variational Bayes approximation is employed for tractable posterior inference, overcoming computational bottlenecks.