Clustering individuals based on multivariate EMA time-series data

TL;DR

This paper explores clustering multivariate EMA time-series data to identify meaningful individual groups, emphasizing the importance of data representation and evaluating various clustering methods for stability and quality.

Contribution

It introduces a framework for clustering EMA time-series data, comparing different methods and distance measures, and highlights the effectiveness of kernel-based clustering in this context.

Findings

Kernel-based clustering methods showed promising results.

Efficient data representations are crucial for successful clustering.

Clustering stability and quality vary across methods.

Abstract

In the field of psychopathology, Ecological Momentary Assessment (EMA) methodological advancements have offered new opportunities to collect time-intensive, repeated and intra-individual measurements. This way, a large amount of data has become available, providing the means for further exploring mental disorders. Consequently, advanced machine learning (ML) methods are needed to understand data characteristics and uncover hidden and meaningful relationships regarding the underlying complex psychological processes. Among other uses, ML facilitates the identification of similar patterns in data of different individuals through clustering. This paper focuses on clustering multivariate time-series (MTS) data of individuals into several groups. Since clustering is an unsupervised problem, it is challenging to assess whether the resulting grouping is successful. Thus, we investigate different clustering methods based on different distance measures and assess them for the stability and quality of the derived clusters. These clustering steps are illustrated on a real-world EMA dataset, including 33 individuals and 15 variables. Through evaluation, the results of kernel-based clustering methods appear promising to identify meaningful groups in the data. So, efficient representations of EMA data play an important role in clustering.