Outlier detection using iterative adaptive mini-minimum spanning tree generation with applications on medical data

Frontiers in Physiology

Physiology

Hui Liu¹  Tanja Schultz¹  Jiangwei Li²  Fons J. Verbeek³  Jia Li⁴  Chenxu Wang⁵ 

[1] Cognitive Systems Lab, University of Bremen, Bremen, Germany;Department of Geriatric Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China;Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands;School of Software Engineering, Xi’an Jiaotong University, Xi’an, China;Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands;School of Software Engineering, Xi’an Jiaotong University, Xi’an, China;MOE Key Lab of Intelligent Network and Network Security, Xi’an Jiaotong University, Xi’an, China;

关键词: minimum spanning tree;    outlier detection;    cluster-based outlier detection;    data mining;    medical data;   

DOI  :  10.3389/fphys.2023.1233341

 received in 2023-06-01, accepted in 2023-09-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Li, Li, Wang, Verbeek, Schultz and Liu.