1 research outputs found
Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data (Revised)
Most current clustering based anomaly detection methods use scoring schema
and thresholds to classify anomalies. These methods are often tailored to
target specific data sets with "known" number of clusters. The paper provides a
streaming clustering and anomaly detection algorithm that does not require
strict arbitrary thresholds on the anomaly scores or knowledge of the number of
clusters while performing probabilistic anomaly detection and clustering
simultaneously. This ensures that the cluster formation is not impacted by the
presence of anomalous data, thereby leading to more reliable definition of
"normal vs abnormal" behavior. The motivations behind developing the INCAD
model and the path that leads to the streaming model is discussed.Comment: 13 pages; fixes typos in equations 5,6,9,10 on inference using Gibbs
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