Clustering Individuals Based on Multivariate EMA Time-Series Data

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

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.

Original languageEnglish
Title of host publicationQuantitative Psychology
EditorsMarie Wiberg, Dylan Molenaar, Jorge González, Jee-Seon Kim, Heungsun Hwang
PublisherSpringer, Cham
Pages161-171
Number of pages11
ISBN (Electronic)978-3-031-27781-8
ISBN (Print)978-3-031-27780-1
DOIs
Publication statusPublished - 2023
EventThe 87th Annual Meeting of the Psychometric Society - Bologna, Italy
Duration: 11 Jul 202215 Jul 2022
Conference number: IMPS 2022
https://www.psychometricsociety.org/imps-2022

Publication series

SeriesSpringer Proceedings in Mathematics and Statistics
Volume422
ISSN2194-1009

Conference

ConferenceThe 87th Annual Meeting of the Psychometric Society
Country/TerritoryItaly
CityBologna
Period11/07/2215/07/22
Internet address

Keywords

  • Ecological momentary assessment (EMA)
  • Clustering
  • DTW
  • Global Alignment Kernel

Fingerprint

Dive into the research topics of 'Clustering Individuals Based on Multivariate EMA Time-Series Data'. Together they form a unique fingerprint.

Cite this