Intel Corporation
Iterative supervised identification of non-dominant clusters

Abstract:

A method comprising determining a binary classification value for each of a plurality of data instances based on a first threshold value assigned to each of the plurality of data instances; applying at least one clustering model to a first subset of the plurality of data instances to identify one or more dominant clusters of data instances; determining a second threshold value to assign to a second plurality of data instances that are included within the one or more dominant clusters of data instances; and redetermining a binary classification value for each of the plurality of data instances based on the second threshold value assigned to the second plurality of data instances and the first threshold value, wherein the first threshold value is assigned to at least a portion of data instances of the plurality of data instances that are not included in the second plurality of data instances.