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

arXiv: https://arxiv.org/abs/2210.08212 · HTML: https://mlsanity.com/paper/2210.08212/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution](https://mlsanity.com/paper/2206.07647/) (2022)
- [Explaining Anomalies in Groups with Characterizing Subspace Rules](https://mlsanity.com/paper/1708.05929/) (2017)
- [Robust Contextual Outlier Detection: Where Context Meets Sparsity](https://mlsanity.com/paper/1607.08329/) (2016)
- [gen2Out: Detecting and Ranking Generalized Anomalies](https://mlsanity.com/paper/2109.02704/) (2021)
- [Sparx: Distributed Outlier Detection at Scale](https://mlsanity.com/paper/2206.01281/) (2022)
- [C-AllOut: Catching & Calling Outliers by Type](https://mlsanity.com/paper/2110.08257/) (2021)

## Built on by (most cited first, 5 of 5)

- [Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency](https://mlsanity.com/paper/2301.05412/) (2023)
- [Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors](https://mlsanity.com/paper/2408.13667/) (2024)
- [Randomized PCA Forest for Unsupervised Outlier Detection](https://mlsanity.com/paper/2508.12776/) (2025)
- [McCatch: Scalable Microcluster Detection in Dimensional and Nondimensional Datasets](https://mlsanity.com/paper/2403.08027/) (2024)
- [FRAUDGUESS: Spotting and Explaining New Types of Fraud in Million-Scale Financial Data](https://mlsanity.com/paper/2509.15493/) (2025)

## Abstract (arXiv)

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 underlineDetect outliers with explicit underlineMicro-underlineCluster underlineAssignment. 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.