Finding Outliers in Gaussian Model-based Clustering

Katharine M. Clark and Paul D. McNicholas ()
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Katharine M. Clark: McMaster University
Paul D. McNicholas: McMaster University

Journal of Classification, 2024, vol. 41, issue 2, No 6, 313-337

Abstract: Abstract Clustering, or unsupervised classification, is a task often plagued by outliers. Yet there is a paucity of work on handling outliers in clustering. Outlier identification algorithms tend to fall into three broad categories: outlier inclusion, outlier trimming, and post hoc outlier identification methods, with the former two often requiring pre-specification of the number of outliers. The fact that sample squared Mahalanobis distance is beta-distributed is used to derive an approximate distribution for the log-likelihoods of subset finite Gaussian mixture models. An algorithm is then proposed that removes the least plausible points according to the subset log-likelihoods, which are deemed outliers, until the subset log-likelihoods adhere to the reference distribution. This results in a trimming method, called OCLUST, that inherently estimates the number of outliers.

Keywords: Clustering; Model selection; Mixture models; OCLUST; Outlier (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00357-024-09473-3

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