Applying Clustering to the Classification Problem

Piew Datta

The minimum-distance classifier learns a single mean prototype for each class and uses a nearest neighbor approach for classification. A problem arises when classes cannot be accurately represented using a single prototype; multiple prototypes may be necessary. Our approach is to find groups of examples for each of the classes, generalize these groups into prototypes using a mean representation, and then classify using a nearest neighbor approach.

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