Abstract

Anomaly detection is a crucial data mining problem due to its extensive range of applications. In real-world scenarios, anomalies often exhibit different levels of priority. Unfortunately, existing methods tend to overlook this phenomenon and identify all types of anomalies into a single class. In this paper, we propose a generalized formulation of the anomaly detection problem, which covers not only the conventional anomaly detection task, but also the partial anomaly detection task that is focused on identifying target anomalies of primary interest while intentionally disregarding non-target (low-risk) anomalies. One of the challenges in addressing this problem is the overlap among normal instances and anomalies of different levels of priority, which may cause high false positive rates. Additionally, acquiring a sufficient quantity of all types of labeled non-target anomalies is not always feasible. For this purpose, we present a generalized anomaly detection framework flexible in addressing a broader range of anomaly detection scenarios. Employing a dual-center mechanism to handle relationships among normal instances, non-target anomalies, and target anomalies, the proposed framework significantly reduces the number of false positives caused by class overlap and tackles the challenge of limited amount of labeled data. Extensive experiments conducted on two publicly available datasets from different domains demonstrate the effectiveness, robustness and superior labeled data utilization of the proposed framework. When applied to a real-world application, it exhibits a lift of at least 7.08% in AUPRC compared to the alternatives, showcasing its remarkable practicality.