Abstract
Outliers are deviations from the usual trends of data; to discover interestingness among outliers i.e. finding anomalies which are of real-interest for subject matter experts is an active area of research in data mining and machine learning community. Due to its subjective nature, the definition of what amounts to ‘interesting’ varies between domains and subject matter experts. This paper provides an overview of the current state of quantification for measures of interestingness, using Bayesian Belief Networks as background knowledge. Building up on this foundation, we also provide a process flow for ranking outliers based on subject matter expert's apriori interestingness.
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.
