A method for classifying data involves receiving a set of training data from a
physical process such as a computer network (20). The training data has
attribute data and target data. The target data has a class label associated with
the attribute data. Dummy clusters are derived from centroid coordinates of the
training data associated with the class label (22). Distance measures are
determined between the training data and a plurality of clusters which include
the dummy clusters (24). Real clusters are created in the plurality of clusters
if the training data is closest to a dummy cluster or a cluster having a class
label different than the class label associated with the training data (26).
A closest match between data to be classified and the plurality of clusters is
identified (28) and the data is classified as the class label of the closest
match from the plurality of clusters (30).
