Explaining Clustering of Ecological Momentary Assessment Data Through Temporal and Feature Attention
This work addresses the problem of clustering explainability for researchers in psychopathology, enabling better understanding of mental disorders through interpretable group-level analysis, though it is incremental as it applies existing attention mechanisms to a specific domain.
The paper tackled the challenge of evaluating and explaining clustering in Ecological Momentary Assessment (EMA) data, which lacks true grouping labels, by proposing an attention-based interpretable framework to identify important time-points and variables that distinguish clusters, using a dataset of 187 individuals grouped into 3 clusters to analyze derived importance attributes.
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.