Summary of Explaining Clustering Of Ecological Momentary Assessment Data Through Temporal and Feature Attention, by Mandani Ntekouli et al.
Explaining Clustering of Ecological Momentary Assessment Data Through Temporal and Feature Attention
by Mandani Ntekouli, Gerasimos Spanakis, Lourens Waldorp, Anne Roefs
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel attention-based framework to identify the crucial time-points and variables that differentiate between clusters in Ecological Momentary Assessment (EMA) studies. The authors focus on clustering explainability, aiming to uncover the patterns underlying important segments of EMA data that distinguish across groups. To evaluate their approach, they use an EMA dataset of 187 individuals grouped into three clusters, analyzing attention-based importance attributes at the cluster-, feature-, and individual levels. This framework can facilitate better understanding of mental disorders, discover new insights, and enhance knowledge at an individual level. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make sense of big data collected from people in real-time about their emotions, behaviors, and other factors related to mental health. The authors want to understand why certain groups of people behave similarly or differently. To do this, they use a special method that highlights the most important moments and variables that separate these groups. They test this approach on a large dataset of 187 people grouped into three categories. By analyzing the results, they hope to gain new insights about mental health disorders and how they affect individuals. |
Keywords
» Artificial intelligence » Attention » Clustering
