Longitudinal data clustering methods: A Systematic Review

Message:
Article Type:
Review Article (دارای رتبه معتبر)
Abstract:

In the last few decades, in many research fields, different methods were introduced to discover groups with the same trends in longitudinal data. The clustering process is an unsupervised learning method, which classifies longitudinal data based on different criteria by performing algorithms. The current study was performed with the aim of reviewing various methods of longitudinal data clustering, including two general categories of non-parametric methods and model-based methods. PubMed, SCOPUS, ISI, Ovid, and Google Scholar were searched between 2000 and 2021. According to our systematic review, the non-parametric k-means Clustering Method utilizing Euclidean distance emerges as a leading approach for clustering longitudinal data This research, with an overview of the studies done in the field of clustering, can help researchers as a toolbox to choose various methods of longitudinal data clustering in idea generation and choosing the appropriate method in the classification and analysis of longitudinal data.

Language:
English
Published:
Journal of Biostatistics and Epidemiology, Volume:9 Issue: 4, Autumn 2023
Pages:
396 to 411
https://www.magiran.com/p2779262  
سامانه نویسندگان
  • Corresponding Author (1)
    Abbas Bahrampour
    Professor Biostatistics and epidemiology, Kerman University Of Medical Sciences, Kerman, Iran
    Bahrampour، Abbas
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