clusTransition: An R package for monitoring transition in cluster solutions of temporal datasets

• Published in: PloS one Vol. 17; no. 12; p. e0278146
• Main Authors: Atif, Muhammad, Leisch, Friedrich
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.12.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Cluster Analysis; Clustering; Computer and Information Sciences; Datasets; Marketing; Mathematical statistics; Monitoring; People and Places; Physical Sciences; Programming Languages; Research and Analysis Methods; Social Sciences; Static objects; Workflow; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0278146

Clustering analysis' primary purpose is to divide a dataset into a finite number of segments based on the similarities between items. In recent years, a significant amount of study has focused on the spatio-temporal aspects of clustering. However, clusters are no longer regarded as static objects since changes influence them in the underlying population. This paper describes an R package implementing the MONIC framework for tracing the evolution of clusters extracted from temporal datasets. The name of the package is clusTransition, which stands for Cluster Transition. The algorithm is based on re-clustering cumulative datasets that evolve at successive time-points and monitoring the transitions experienced by the clusters in these clustering solutions. This paper's contribution is to demonstrate how the package clusTransition is developed in the R programming language, and its workflow is discussed using hypothetical and real-life datasets.