In the field of psychopathology, Ecological Momentary Assessment (EMA) studies offer rich individual data on psychopathology-relevant variables (e.g., affect, behavior, etc) in real-time. EMA data is collected dynamically, represented as complex multivariate time series (MTS). Such information is crucial for a better understanding of mental disorders at the individual- and group-level. More specifically, clustering individuals in EMA data facilitates uncovering and studying the commonalities as well as variations of groups in the population. Nevertheless, since clustering is an unsupervised task and true EMA grouping is not commonly available, the evaluation of clustering is quite challenging. An important aspect of evaluation is clustering explainability. Thus, this paper proposes an attention-based interpretable framework to identify the important time-points and variables that play primary roles in distinguishing between clusters. A key part of this study is to examine ways to analyze, summarize, and interpret the attention weights as well as evaluate the patterns underlying the important segments of the data that differentiate across clusters. To evaluate the proposed approach, an EMA dataset of 187 individuals grouped in 3 clusters is used for analyzing the derived attention-based importance attributes. More specifically, this analysis provides the distinct characteristics at the cluster-, feature- and individual level. Such clustering explanations could be beneficial for generalizing existing concepts of mental disorders, discovering new insights, and even enhancing our knowledge at an individual level.

翻译：在精神病理学领域，生态瞬时评估（EMA）研究提供了关于精神病理相关变量（如情感、行为等）的实时丰富个体数据。EMA数据以动态方式采集，表现为复杂的多元时间序列（MTS）。这些信息对于从个体和群体层面更好地理解精神障碍至关重要。具体而言，对EMA数据中的个体进行聚类有助于揭示和研究群体中的共同特征及差异。然而，由于聚类属于无监督任务且真实的EMA分组通常不可获得，因此聚类评估极具挑战性。评估的一个重要方面是聚类可解释性。为此，本文提出了一种基于注意力的可解释框架来识别在区分聚类中起主要作用的关键时间点和变量。本研究的核心在于探索分析、总结和解释注意力权重的方法，并评估区分不同聚类的数据重要片段背后的模式。为评估所提方法，我们使用了一个包含187名个体且分为3个聚类的EMA数据集，以分析基于注意力的重要性属性。具体分析从聚类层面、特征层面和个体层面提供了差异性特征。此类聚类解释有助于概括现有精神障碍概念、发现新洞见，甚至能在个体层面增进我们的认知。