Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
