| Summary: | 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.
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