Abstract
Outlier detection becomes more and more important in our real life, such as network intrusion detection and credit card fraud detection, etc. In this paper, a weight-based method is proposed for inside outlier detection. According to the concepts of density and volume information, the weight is defined and introduced to construct a new measure of outlier-ness. Firstly, the total weight of a given object p and its neighbors is computed via their volume and average density. Then the estimated weight of the neighbors is obtained via the neighborhood's volume and p's density. If the total weight is not close to the estimated weight, p is an outlier. The weight-based method shows more superiority in inside outlier detection than LOF in low dimensions. Moreover, the proposed method performs as well as LOD in a high-dimensional space or when no inside outlier exists.
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.
