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

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  • Muhammad Atif
  • Friedrich Leisch

Abstract

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.

Suggested Citation

  • Muhammad Atif & Friedrich Leisch, 2022. "clusTransition: An R package for monitoring transition in cluster solutions of temporal datasets," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-20, December.
    • Handle: RePEc:plo:pone00:0278146
    DOI: 10.1371/journal.pone.0278146
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    References listed on IDEAS

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    1. Weiliang Qiu & Harry Joe, 2006. "Generation of Random Clusters with Specified Degree of Separation," Journal of Classification, Springer;The Classification Society, vol. 23(2), pages 315-334, September.
    2. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
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