C-AllOut: Catching & Calling Outliers by Type

• URL: http://arxiv.org/abs/2110.08257v1
• Date: Wed, 13 Oct 2021 14:25:52 GMT
• Title: C-AllOut: Catching & Calling Outliers by Type
• Authors: Guilherme D. F. Silva, Leman Akoglu, Robson L. F. Cordeiro
• Abstract summary: C-AllOut is a novel outlier detector that annotates outliers by type. It is parameter-free and scalable, besides working only with pairwise similarities (or distances) when it is needed.
• Score: 10.69970450827617
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Given an unlabeled dataset, wherein we have access only to pairwise similarities (or distances), how can we effectively (1) detect outliers, and (2) annotate/tag the outliers by type? Outlier detection has a large literature, yet we find a key gap in the field: to our knowledge, no existing work addresses the outlier annotation problem. Outliers are broadly classified into 3 types, representing distinct patterns that could be valuable to analysts: (a) global outliers are severe yet isolate cases that do not repeat, e.g., a data collection error; (b) local outliers diverge from their peers within a context, e.g., a particularly short basketball player; and (c) collective outliers are isolated micro-clusters that may indicate coalition or repetitions, e.g., frauds that exploit the same loophole. This paper presents C-AllOut: a novel and effective outlier detector that annotates outliers by type. It is parameter-free and scalable, besides working only with pairwise similarities (or distances) when it is needed. We show that C-AllOut achieves on par or significantly better performance than state-of-the-art detectors when spotting outliers regardless of their type. It is also highly effective in annotating outliers of particular types, a task that none of the baselines can perform.

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