# Explaining Clustering of Ecological Momentary Assessment Data Through Temporal and Feature Attention

arXiv: https://arxiv.org/abs/2405.04854 · HTML: https://mlsanity.com/paper/2405.04854/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [TimeSHAP: Explaining Recurrent Models through Sequence Perturbations](https://mlsanity.com/paper/2012.00073/) (2020)
- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models](https://mlsanity.com/paper/2109.08438/) (2021)
- [Feature Importance Estimation with Self-Attention Networks](https://mlsanity.com/paper/2002.04464/) (2020)
- [How to Find a Good Explanation for Clustering?](https://mlsanity.com/paper/2112.06580/) (2021)
- [Feature Importance for Time Series Data: Improving KernelSHAP](https://mlsanity.com/paper/2210.02176/) (2022)
- [Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data](https://mlsanity.com/paper/2204.01689/) (2022)
- [XClusters: Explainability-first Clustering](https://mlsanity.com/paper/2209.10956/) (2022)
- [Interpretable Time Series Clustering Using Local Explanations](https://mlsanity.com/paper/2208.01152/) (2022)
- [Model-based Clustering of Individuals' Ecological Momentary Assessment Time-series Data for Improving Forecasting Performance](https://mlsanity.com/paper/2310.07491/) (2023)

## Built on by (most cited first, 1 of 1)

- [GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering](https://mlsanity.com/paper/2603.05318/) (2026)

## Abstract (arXiv)

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.