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

Cristina Tortora () and Francesco Palumbo ()
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Cristina Tortora: San José State University
Francesco Palumbo: University of Naples Federico II

Computational Statistics, 2025, vol. 40, issue 2, No 22, 1123-1146

Abstract: 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.

Keywords: Probabilistic distance clustering; Soft clustering; Mixed-type data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01490-5

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