We introduce a repulsive mixture model to cluster observation units represented by multivariate functional data, based on similarity of curve shapes and individual-specific covariates. We propose a repulsive prior distribution for the component-specific location parameters that depends on a B-spline curve-tailored distance, extending existent repulsive priors to the context of multivariate functional data. The proposed model favors the identification of well-differentiated clusters, avoiding the presence of redundant ones. To sample from the posterior distribution, we propose an MCMC algorithm that includes a novel split-merge step that significantly improves the chain mixing. Different features of the proposed model, including the effects of repulsion and covariates in the clustering, are evaluated through simulation. The proposed model is fitted to analyze Chronic Ankle Instability (CAI) data, focusing on identifing individuals with similar types of physical dysfunctions based on the similarity of movement patterns.

翻译：本文提出一种互斥混合模型，用于对以多变量函数数据表示的观测单元进行聚类，该聚类基于曲线形状的相似性和个体特异性协变量。我们为组分特定位置参数提出了一种依赖于B样条曲线定制距离的互斥先验分布，将现有互斥先验扩展到多变量函数数据情境。所提模型有利于识别区分度良好的聚类，避免冗余聚类的出现。为从后验分布中采样，我们提出了一种包含新颖分裂-合并步骤的MCMC算法，该步骤显著改善了链的混合性能。通过模拟研究评估了所提模型的不同特征，包括互斥效应和协变量在聚类中的作用。我们将所提模型应用于慢性踝关节不稳定（CAI）数据分析，重点在于基于运动模式相似性识别具有相似生理功能障碍类型的个体。