Method, apparatus, and computer-readable medium for efficiently classifying a data object of unknown type

An apparatus for efficiently classifying a data object of unknown type, the apparatus comprising:

• one or more processors;
• and
• one or more memories operatively coupled to at least one of the one or more processors and having instructions stored thereon that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to:
• represent the data object as a data object vector in a vector space, each dimension of the data object vector corresponding to a different feature of the data object;
• determine a distance between the data object vector and each of a plurality of centroids of a plurality of data domain clusters in the vector space, each data domain cluster comprising a plurality of data domain vectors representing a plurality of data domains, wherein each data domain vector comprises a multidimensional vector reflecting a plurality of feature values of a plurality of features of a data domain model corresponding to a data domain represented by the data domain vector;
• sort the plurality of data domain clusters according to their respective distances of the plurality of data domain clusters to the data object vector;
• and
• iteratively apply one or more data domain classifiers corresponding to one or more data domains represented in a closest data domain cluster in the plurality of sorted data domain clusters to the data object.

At least one non-transitory computer-readable medium storing computer-readable instructions for postal address identification that, when executed by one or more computing devices, cause at least one of the one or more computing devices to:

• represent the data object as a data object vector in a vector space, each dimension of the data object vector corresponding to a different feature of the data object;
• determine a distance between the data object vector and each of a plurality of centroids of a plurality of data domain clusters in the vector space, each data domain cluster comprising a plurality of data domain vectors representing a plurality of data domains, wherein each data domain vector comprises a multidimensional vector reflecting a plurality of feature values of a plurality of features of a data domain model corresponding to a data domain represented by the data domain vector;
• sort the plurality of data domain clusters according to respective distances of the plurality of data domain clusters to the data object vector;
• and
• iteratively apply one or more data domain classifiers corresponding to one or more data domains represented in a closest data domain cluster in the plurality of sorted data domain clusters to the data object.