An efficient anomaly detection method for uncertain data based on minimal rare patterns with the consideration of anti-monotonic constraints

Abstract

The pattern-based anomaly detection method has obtained more attention since it was proposed. This is due to its ability to fully identify anomalies by considering two key features, including appearing rarely and deviating from most data elements. Although most pattern-based anomaly detection methods identify the anomalies from full data space, the process involved in performing this functionality is very time-consuming. Therefore, to solve this problem, this paper introduces a novel method, namely minimal rare pattern-based anomaly detection method through considering anti-monotonic constraints (MRPAC), for identifying anomalies in uncertain data. MRPAC detects the anomalies from a small scale of uncertain data that satisfy preset anti-monotonic constraints by mining constrained minimal rare patterns, thus, the time efficiency is increased. In addition, MRPAC also defines multiple deviation factors to compute the anomaly score for all transactions, to accurately discover potential anomalies through sorting their anomaly scores. Extensive experimental outcomes indicate that the MRPAC significantly outperforms five state-of-the-art pattern-based anomaly methods in terms of detection accuracy and time efficiency, and obtains good scalability.