Abstract/Description
Unsupervised data mining techniques require human intervention for understanding and analysis of the clustering results. This becomes an issue in dynamic users/applications and there is a need for real-time decision making and interpretation. In this paper we will present an approach to automate the annotation of results obtained from data stream clustering to facilitate interpreting that whether the given cluster is an anomaly or not. We use fuzzy logic to label the data. The results will be obtained on the basis of density function & the number of elements in a certain cluster.
Keywords
Fuzzy logic, Intrusion detection, Data mining, Decision making, Particle measurements, Fuzzy systems, Monitoring, Uncertainty, Expert systems
Session Theme
Artificial Intelligence – II
Session Type
Other
Session Chair
Dr. Sharifullah Khan
Start Date
16-8-2009 12:40 PM
End Date
16-8-2009 1:00 PM
Recommended Citation
Khan, M. U. (2009). Artificial Intelligence – II: Anomaly detection in data streams using fuzzy logic. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2009/2009/26
Artificial Intelligence – II: Anomaly detection in data streams using fuzzy logic
Unsupervised data mining techniques require human intervention for understanding and analysis of the clustering results. This becomes an issue in dynamic users/applications and there is a need for real-time decision making and interpretation. In this paper we will present an approach to automate the annotation of results obtained from data stream clustering to facilitate interpreting that whether the given cluster is an anomaly or not. We use fuzzy logic to label the data. The results will be obtained on the basis of density function & the number of elements in a certain cluster.
