  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.
