[MMOD Altorighm] Outlier detection using iterative adaptive mini-minimum spanning tree generation with applications on medical data
Date:2025-05-23 Hits:
Impact Factor:4.0
DOI Number:10.3389/fphys.2023.1233341
Affiliation of Author(s):Xi'an Jiaotong University; Leiden University; University of Bremen
Journal:Frontiers in Physiology
Place of Publication:Switzerland
Key Words:minimum spanning tree; outlier detection; cluster-based outlier detection; data mining; medical data
Abstract:As an important technique for data pre-processing, outlier detection plays a crucial role in various real applications and has gained substantial attention, especially in medical fields. Despite the importance of outlier detection, many existing methods are vulnerable to the distribution of outliers and require prior knowledge, such as the outlier proportion. To address this problem to some extent, this article proposes an adaptive mini-minimum spanning tree-based outlier detection (MMOD) method, which utilizes a novel distance measure by scaling the Euclidean distance. For datasets containing different densities and taking on different shapes, our method can identify outliers without prior knowledge of outlier percentages. The results on both real-world medical data corpora and intuitive synthetic datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art methods.
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:14
Page Number:1233341
ISSN No.:1664-042X
Translation or Not:no
Date of Publication:2023-10-13
Included Journals:SCI
Publication links:https://www.frontiersin.org/articles/10.3389/fphys.2023.1233341