[MS2OD Algorithm] MS2OD: Outlier Detection Using Minimum Spanning Tree and Medoid Selection
Date:2025-05-17 Hits:
Impact Factor:6.8
DOI Number:10.1088/2632-2153/ad2492
Affiliation of Author(s):University of Bremen; Nanjing University of Information Science and Technology
Journal:Machine Learning: Science and Technology
Place of Publication:England
Key Words:outlier detection; data mining; medical data; machine learning; clustering; minimum spanning tree; medoid selection
Abstract:As an essential task in data mining, outlier detection identifies abnormal patterns in numerous applications, among which clustering-based outlier detection is one of the most popular methods for its effectiveness in detecting cluster-related outliers, especially in medical applications. This article presents an advanced method to extract cluster-based outliers by employing a scaled minimum spanning tree (MST) data structure and a new medoid selection method: 1. we compute a scaled MST and iteratively cut the current longest edge to obtain clusters; 2. we apply a new medoid selection method, considering the noise effect to improve the quality of cluster-based outlier identification. The experimental results on real-world data, including extensive medical corpora and other semantically meaningful datasets, demonstrate the wide applicability and outperforming metrics of the proposed method.
Note:ESI Hot Paper (top 0.1%) and Highly-Cited Paper (top 1%).
All the Authors:Jia Li, Jiangwei Li, Chenxu Wang, Fons J. Verbeek*, Tanja Schultz, Hui Liu*
Indexed by:Journal paper
Discipline:Engineering
Document Type:J
Volume:5
Page Number:015025
Translation or Not:no
Date of Publication:2024-02-12
Included Journals:SCI
Publication links:https://iopscience.iop.org/article/10.1088/2632-2153/ad2492