Finding Outliers in Gaussian Model-Based Clustering

• URL: http://arxiv.org/abs/1907.01136v6
• Date: Thu, 30 May 2024 16:26:06 GMT
• Title: Finding Outliers in Gaussian Model-Based Clustering
• Authors: Katharine M. Clark, Paul D. McNicholas,
• Abstract summary: Clustering, or unsupervised classification, is a task often plagued by outliers. There is a paucity of work on handling outliers in clustering.
• Score: 1.0435741631709405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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