An efficient strategy to detect outlier transactions for knowledge mining

Instant identification of outlier patterns is very important in modern-day engineering problems such as credit card fraud detection and network intrusion detection. Most previous studies focused on finding outliers that are hidden in numerical datasets. Unfortunately, those outlier detection methods were not directly applicable to real life transaction databases. Although a limited literature presented methods to find outliers in the transaction datasets, they did not address what really caused the transactions to become abnormal. In this paper, an improved framework is proposed to identify the outlier transactions as well as to find the most possible items that induce the abnormal transactions. Several definitions are defined as prerequisite for outlier detection. Efficiency comparisons with previous work are also done to verify the effectiveness of the proposed framework.