D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments

TL;DR

D.MCA is a novel outlier detection method that simultaneously identifies outliers and explicitly assigns them to micro-clusters without prior knowledge of cluster count, improving detection accuracy and interpretability.

Contribution

The paper introduces D.MCA, a new iterative in-house method for outlier detection and micro-cluster assignment that overcomes hyperparameter sensitivity and clustering challenges.

Findings

Outperforms 8 state-of-the-art methods on 16 datasets.

Effectively handles clustered and scattered outliers.

Robust to hyperparameter variations.

Abstract

How can we detect outliers, both scattered and clustered, and also explicitly assign them to respective micro-clusters, without knowing apriori how many micro-clusters exist? How can we perform both tasks in-house, i.e., without any post-hoc processing, so that both detection and assignment can benefit simultaneously from each other? Presenting outliers in separate micro-clusters is informative to analysts in many real-world applications. However, a na\"ive solution based on post-hoc clustering of the outliers detected by any existing method suffers from two main drawbacks: (a) appropriate hyperparameter values are commonly unknown for clustering, and most algorithms struggle with clusters of varying shapes and densities; (b) detection and assignment cannot benefit from one another. In this paper, we propose D.MCA to $\underline{D}$etect outliers with explicit $\underline{M}$icro-$\underline{C}$luster $\underline{A}$ssignment. Our method performs both detection and assignment iteratively, and in-house, by using a novel strategy that prunes entire micro-clusters out of the training set to improve the performance of the detection. It also benefits from a novel strategy that avoids clustered outliers to mask each other, which is a well-known problem in the literature. Also, D.MCA is designed to be robust to a critical hyperparameter by employing a hyperensemble "warm up" phase. Experiments performed on 16 real-world and synthetic datasets demonstrate that D.MCA outperforms 8 state-of-the-art competitors, especially on the explicit outlier micro-cluster assignment task.