1. A computer-based method for discovering patterns in financial transaction card transaction data for determining group memberships of a merchant within the transaction data, said method comprising: storing transaction data within at least one database, wherein the transaction data includes data relating to merchants accepting transaction cards for payment; retrieving the transaction data by a first computer coupled to the at least one database; using at least one prediction algorithm and the retrieved transaction data to predict a plurality of group memberships of a merchant within one or more groupings of merchants wherein the algorithm is processed by the first computer; generating meta-data describing each prediction outputted by the at least one prediction algorithm, wherein the meta-data is generated by the at least one algorithm; inputting the plurality of predicted group memberships for the merchant and the meta-data describing each prediction into a data mining application executed on a second computer; assigning, by the second computer, a confidence score to each predicted group membership by the data mining application based at least in part on the predicted group memberships and the meta-data, wherein the confidence score represents a likelihood the merchant is actually associated with the corresponding predicted group membership; and outputting, by the second computer, the group membership prediction with the highest confidence score as a final membership prediction for the merchant.
1. A computer-based method for discovering patterns in financial transaction card transaction data for determining group memberships of a merchant within the transaction data, said method comprising: storing transaction data within at least one database, wherein the transaction data includes data relating to merchants accepting transaction cards for payment; retrieving the transaction data by a first computer coupled to the at least one database; using at least one prediction algorithm and the retrieved transaction data to predict a plurality of group memberships of a merchant within one or more groupings of merchants wherein the algorithm is processed by the first computer; generating meta-data describing each prediction outputted by the at least one prediction algorithm, wherein the meta-data is generated by the at least one algorithm; inputting the plurality of predicted group memberships for the merchant and the meta-data describing each prediction into a data mining application executed on a second computer; assigning, by the second computer, a confidence score to each predicted group membership by the data mining application based at least in part on the predicted group memberships and the meta-data, wherein the confidence score represents a likelihood the merchant is actually associated with the corresponding predicted group membership; and outputting, by the second computer, the group membership prediction with the highest confidence score as a final membership prediction for the merchant. 13. 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 predicting, utilizing a numerical signature algorithm and an observed tendency for merchants belonging to the same group to diverge from a distribution in a relatively consistent manner, a group of locations that have a similar numeric distribution as compared to each merchant location, wherein location is based on a calculated similarity, the similarity based on field values and field tokenized values within the database.