Finding interesting outliers - a Belief Network based approach
- Resource Type
- Conference
- Authors
- Masood, Adnan; Li, Wei
- Source
- SoutheastCon 2015. :1-7 Apr, 2015
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Probabilistic logic
Bayes methods
Sensitivity
Graphical models
Data mining
Uncertainty
Joints
Outlier Analysis
Interestingness Measures
Bayesian Belief Network
Probabilistic Graphical Models
- Language
- ISSN
- 1091-0050
1558-058X
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.