Continuous outlier detection in data streams: an extensible framework and state-of-the-art algorithms

@inproceedings{Georgiadis2013ContinuousOD,
  title={Continuous outlier detection in data streams: an extensible framework and state-of-the-art algorithms},
  author={Dimitrios Georgiadis and Maria Kontaki and A. Gounaris and A. Papadopoulos and K. Tsichlas and Y. Manolopoulos},
  booktitle={SIGMOD '13},
  year={2013}
}
Anomaly detection is an important data mining task, aiming at the discovery of elements that show significant diversion from the expected behavior; such elements are termed as outliers. One of the most widely employed criteria for determining whether an element is an outlier is based on the number of neighboring elements within a fixed distance (R), against a fixed threshold (k). Such outliers are referred to as distance-based outliers and are the focus of this work. In this demo, we show both… Expand
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