Anomaly Pattern Detection on Data Streams
Cheong Hee Park · 2018
A data stream is a sequence of data generated continuously over time. A data stream is too big to be saved in memory and its underlying data distribution may change over time. Outlier detection aims to find data instances which significantly deviate from the underlying data distribution. While outlier detection is performed at an individual instance level, anomalous pattern detection involves detecting a point in time where the behavior of the data becomes unusual and differs from normal behavior. Most outlier detection methods work in unsupervised mode, where the class labels of the data samples are not known. Alternatively, concept drift detection methods find a drifting point in the streaming data and try to adapt the model to the new emerging pattern. In this paper, we provide a review of outlier detection, anomaly pattern detection and concept drift detection approaches for streaming data.