{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/d-mca-outlier-detection-with-explicit-micro","title":"D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments","arxiv_id":"2210.08212","date":"2022-10-15","proceeding":null,"authors":["Shuli Jiang","Robson Leonardo Ferreira Cordeiro","Leman Akoglu"],"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.","url_abs":"https://arxiv.org/abs/2210.08212v1","url_pdf":"https://arxiv.org/pdf/2210.08212v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"d-mca-outlier-detection-with-explicit-micro","repo_url":"https://github.com/11hifish/d.mca","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}