A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Further Exploration of the Dendritic Cell Algorithm: Antigen Multiplier and Time Windows
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Geometrical insights into the dendritic cell algorithm
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A Sense of `Danger' for Windows Processes
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
The fast recursive row-Householder subspace tracking algorithm
Signal Processing
Data stream anomaly detection through principal subspace tracking
Proceedings of the 2010 ACM Symposium on Applied Computing
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
Quiet in class: classification, noise and the dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Fast and Stable Subspace Tracking
IEEE Transactions on Signal Processing
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This paper begins by stating that the underlying concepts of signals and antigen used by the Dendritic Cell Algorithm are too abstract and arbitrary to be of use in real world applications as they stand. To address this, these concepts are more explicitly defined within a specific application area, namely that of data stream analysis. These new definitions are based around the outputs of the Change Point Detecting Subspace Tracker (CD-ST), a recently developed algorithm for detecting key change points across multiple data streams. Preliminary results demonstrate the utility of this new definition for antigen. The paper concludes by laying the theoretical groundwork for a novel anomaly detection framework for use in data streaming applications. The underlying methodology is to perform anomaly detection via the detection and classification of key change points that occur across the multiple data streams monitored.