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Conditional Anomaly Detection
- Source :
- IEEE Transactions on Knowledge and Data Engineering. 19:631-645
- Publication Year :
- 2007
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2007.
-
Abstract
- When anomaly detection software is used as a data analysis tool, finding the hardest-to-detect anomalies is not the most critical task. Rather, it is often more important to make sure that those anomalies that are reported to the user are in fact interesting. If too many unremarkable data points are returned to the user labeled as candidate anomalies, the software can soon fall into disuse. One way to ensure that returned anomalies are useful is to make use of domain knowledge provided by the user. Often, the data in question includes a set of environmental attributes whose values a user would never consider to be directly indicative of an anomaly. However, such attributes cannot be ignored because they have a direct effect on the expected distribution of the result attributes whose values can indicate an anomalous observation. This paper describes a general purpose method called conditional anomaly detection for taking such differences among attributes into account, and proposes three different expectation-maximization algorithms for learning the model that is used in conditional anomaly detection. Experiments with more than 13 different data sets compare our algorithms with several other more standard methods for outlier or anomaly detection
- Subjects :
- Computer science
business.industry
Anomaly (natural sciences)
computer.software_genre
Machine learning
Computer Science Applications
Information extraction
Computational Theory and Mathematics
Outlier
Anomaly detection
Data mining
Artificial intelligence
Set (psychology)
business
computer
Information Systems
Subjects
Details
- ISSN :
- 10414347
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Knowledge and Data Engineering
- Accession number :
- edsair.doi...........48f6c5d0a7ebcc7a290834d13a03e56f