  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.
