An enriched mixture model for functional clustering

TL;DR

This paper introduces an enriched Dirichlet mixture model for functional data clustering that incorporates prior shape knowledge, controls model complexity, and offers interpretability, with efficient inference via variational Bayes.

Contribution

It presents a novel enriched Bayesian nonparametric model that effectively integrates functional constraints and simplifies the clustering process.

Findings

Model effectively incorporates prior shape constraints.

Provides interpretable clustering results.

Employs variational Bayes for efficient computation.

Abstract

There is an increasingly rich literature about Bayesian nonparametric models for clustering functional observations. However, most of the 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. To clarify the underlying partition mechanism, we characterize the prior process through a P\'olya urn scheme. These features lead to a very interpretable clustering method compared to available techniques. To overcome computational bottlenecks, we employ a variational Bayes approximation for tractable posterior inference.