A Neural Network Anomaly Detector Using the Random Cluster Model
This work addresses anomaly detection in data classification, but appears incremental as it combines existing methods like K-means and regression with a new theoretical bound.
The paper tackled anomaly detection by using a random cluster model to define an upper bound on a distance measure for classification, enabling identification of anomalous classes and individual anomalies, with a neural network storing decision surfaces for offline recall.
The random cluster model is used to define an upper bound on a distance measure as a function of the number of data points to be classified and the expected value of the number of classes to form in a hybrid K-means and regression classification methodology, with the intent of detecting anomalies. Conditions are given for the identification of classes which contain anomalies and individual anomalies within identified classes. A neural network model describes the decision region-separating surface for offline storage and recall in any new anomaly detection.