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FPDclustering: a comprehensive R package for probabilistic distance clustering based methods

Author

Listed:
  • Cristina Tortora

    (San José State University)

  • Francesco Palumbo

    (University of Naples Federico II)

Abstract

Data clustering has a long history and refers to a vast range of models and methods that exploit the ever-more-performing numerical optimization algorithms and are designed to find homogeneous groups of observations in data. In this framework, the probability distance clustering (PDC) family methods offer a numerically effective alternative to model-based clustering methods and a more flexible opportunity in the framework of geometric data clustering. Given n J-dimensional data vectors arranged in a data matrix and the number K of clusters, PDC maximizes the joint density function that is defined as the sum of the products between the distance and the probability, both of which are measured for each data vector from each center. This article shows the capabilities of the PDC family, illustrating the R package FPDclustering.

Suggested Citation

  • Cristina Tortora & Francesco Palumbo, 2025. "FPDclustering: a comprehensive R package for probabilistic distance clustering based methods," Computational Statistics, Springer, vol. 40(2), pages 1123-1146, February.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:2:d:10.1007_s00180-024-01490-5
    DOI: 10.1007/s00180-024-01490-5
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    References listed on IDEAS

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    1. Felix Mbuga & Cristina Tortora, 2021. "Spectral Clustering of Mixed-Type Data," Stats, MDPI, vol. 5(1), pages 1-11, December.
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