Abstract
Introduction: 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. Methods: In this research, to obtain related scientific articles, PubMed, Science Direct Scopus, ISI, and Google Scholar were searched between 2000 and 2021. Results: According to our systematic review, the non-parametric k-means Clustering Method utilizing Euclidean distance emerges as a leading approach for clustering longitudinal data. Conclusion: 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.
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