pivmet: Pivotal Methods for Bayesian Relabelling and k-Means Clustering

Leonardo Egidi; Roberta Pappadà; Francesco Pauli; Nicola Torelli

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arXiv · 2103.16948

pivmet: Pivotal Methods for Bayesian Relabelling and k-Means Clustering

Leonardo Egidi·Roberta Pappadà·Francesco Pauli·Nicola Torelli

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Abstract

The identification of groups' prototypes, i.e. elements of a dataset that represent different groups of data points, may be relevant to the tasks of clustering, classification and mixture modeling. The R package pivmet presented in this paper includes different methods for extracting pivotal units from a dataset. One of the main applications of pivotal methods is a Markov Chain Monte Carlo (MCMC) relabelling procedure to solve the label switching in Bayesian estimation of mixture models. Each method returns posterior estimates, and a set of graphical tools for visualizing the output. The package offers JAGS and Stan sampling procedures for Gaussian mixtures, and allows for user-defined priors' parameters. The package also provides functions to perform consensus clustering based on pivotal units, which may allow to improve classical techniques (e.g. k-means) by means of a careful seeding. The paper provides examples of applications to both real and simulated datasets.

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Leonardo Egidi, Roberta Pappadà, Francesco Pauli, Nicola Torelli. 2021-03-31. pivmet: Pivotal Methods for Bayesian Relabelling and k-Means Clustering. https://arxiv.org/abs/2103.16948

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