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.

基于模式的异常检测方法自提出以来就受到了越来越多的关注。这是因为它能够通过考虑两个关键特征来完全识别异常，包括很少出现和偏离大多数数据元素。尽管大多数基于模式的异常检测方法从完整数据空间中识别异常，但执行此功能所涉及的过程非常耗时。因此，为了解决这个问题，本文介绍了一种新的方法，即中号inimal - [R是p attern基于异常检测方法通过考虑一个NTI单调Ç约束（MRPAC），用于识别不确定数据中的异常。MRPAC通过挖掘受约束的最小稀有模式，从满足预设反单调约束的小规模不确定数据中检测异常，从而提高时间效率。此外，MRPAC 还定义了多个偏差因素来计算所有交易的异常分数，通过对其异常分数进行排序来准确发现潜在的异常。大量实验结果表明，MRPAC 在检测精度和时间效率方面明显优于五种最先进的基于模式的异常方法，并获得了良好的可扩展性。