4. A computer-based method according to claim 1 wherein using at least one prediction algorithm and the retrieved transaction data to predict a plurality of group memberships comprises: tokenizing at least one field in the database; calculating an inverse document frequency for all tokenized field values within the database; computing a sparse matrix of weight metrics for each database field value and each tokenized database field value; and generating a prediction by joining a given database field location to every other database field location based on one or more of field type and field value utilizing the sparse matrix.
4. A computer-based method according to claim 1 wherein using at least one prediction algorithm and the retrieved transaction data to predict a plurality of group memberships comprises: tokenizing at least one field in the database; calculating an inverse document frequency for all tokenized field values within the database; computing a sparse matrix of weight metrics for each database field value and each tokenized database field value; and generating a prediction by joining a given database field location to every other database field location based on one or more of field type and field value utilizing the sparse matrix. 5. A computer-based method according to claim 4 wherein the sparse matrix includes a merchant category code, an Interbank card association (ICA) code, a business region, a merchant name, a merchant phone number, an acquiring merchant identifier, a tier merchant identifier, a merchant legal name, and a federal tax identifier.