 Bayesian nonparametric clustering as a community detection problemStefano F. Tonellato Computational Statistics & Data Analysis, 2020, vol. 152, issue C Abstract: A wide class of Bayesian nonparametric priors leads to the representation of the distribution of the observable variables as a mixture density with an infinite number of components. Such a representation induces a clustering structure in the data. However, due to label switching, cluster identification is not straightforward a posteriori and some post-processing of the MCMC output is usually required. Alternatively, observations can be mapped on a weighted undirected graph, where each node represents a sample item and edge weights are given by the posterior pairwise similarities. It is shown how, after building a particular random walk on such a graph, it is possible to apply a community detection algorithm, known as map equation, leading to the minimisation of the expected description length of the partition. A relevant feature of this method is that it allows for the quantification of the posterior uncertainty of the classification. Keywords: Dirichlet process priors; Mixture models; Community detection; Entropy; Clustering uncertainty (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:152:y:2020:i:c:s0167947320301353 DOI: 10.1016/j.csda.2020.107044 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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