System and method for classifying unknown data patterns in multi-variate feature space

摘要

A system and method for classifying unknown data patterns in a multi-variate feature space. An observer 12 evaluates the closeness of the data to any one of a plurality of known classes in a multi-variate feature space. A classifier 14 classifies the evaluated data into one of the plurality of known classes. A flagger 16 flags data having an unknown classification. A label requester 18 requests that the flagged data be interpreted into either a new class or one of the plurality of known classes. If the interpretation is a new fault, then a new class adder 22 sets up a new cluster representative of the fault and inputs the new class to the classifier 14. Alternatively, if the interpretation is that the flagged data belongs to one of the plurality of known classes, then a class resetter 24 resets the boundaries of this class and informs the classifier 14 of the change.