MS2OD: outlier detection using minimum spanning tree and 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.

Keywords

• outlier detection
• data mining
• medical data
• machine learning
• clustering
• minimum spanning tree