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

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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. Martina Sundqvist & Julien Chiquet & Guillem Rigaill, 2023. "Adjusting the adjusted Rand Index," Computational Statistics, Springer, vol. 38(1), pages 327-347, March.
    2. Cristina Tortora & Mireille Gettler Summa & Marina Marino & Francesco Palumbo, 2016. "Factor probabilistic distance clustering (FPDC): a new clustering method," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 441-464, December.
    3. Alexander H. Foss & Marianthi Markatou & Bonnie Ray, 2019. "Distance Metrics and Clustering Methods for Mixed‐type Data," International Statistical Review, International Statistical Institute, vol. 87(1), pages 80-109, April.
    4. Tue Tjur, 2011. "Statistics in the computer age: personal reflections," Computational Statistics, Springer, vol. 26(3), pages 371-379, September.
    5. Adi Ben-Israel & Cem Iyigun, 2008. "Probabilistic D-Clustering," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 5-26, June.
    6. Vichi, Maurizio & Kiers, Henk A. L., 2001. "Factorial k-means analysis for two-way data," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 49-64, July.
    7. Felix Mbuga & Cristina Tortora, 2021. "Spectral Clustering of Mixed-Type Data," Stats, MDPI, vol. 5(1), pages 1-11, December.
    8. Olvi L. Mangasarian & W. Nick Street & William H. Wolberg, 1995. "Breast Cancer Diagnosis and Prognosis Via Linear Programming," Operations Research, INFORMS, vol. 43(4), pages 570-577, August.
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