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影响因子：6.8

DOI码：10.1088/2632-2153/ad2492

所属单位：University of Bremen; Nanjing University of Information Science and Technology

发表刊物：Machine Learning: Science and Technology

刊物所在地：England

关键字：outlier detection; data mining; medical data; machine learning; clustering; minimum spanning tree; medoid selection

摘要：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.

备注：ESI Hot Paper (top 0.1%) and Highly-Cited Paper (top 1%).

全部作者：Jia Li, Jiangwei Li, Chenxu Wang, Fons J. Verbeek*, Tanja Schultz, Hui Liu*

论文类型：期刊论文

学科门类：工学

文献类型：J

卷号：5

页面范围：015025

是否译文：否

发表时间：2024-02-12

收录刊物：SCI

发布刊物链接：https://iopscience.iop.org/article/10.1088/2632-2153/ad2492