Patent ID: 7895139

Claim:
A computer implemented method of data mining transactional data through data spiders implementing genetic algorithms, comprising the steps of: programmatically creating groups of variables from parameterized transactional data; using a naïve Bayes model to calculate score cards for each group; determining group divergence by naïve Bayes scores and compiling scorecards quantifying the divergence, wherein divergence measures the ability of a naïve Bayes score to separate two outcome classes of a binary target variable; determining fitness of each group by the quantified divergence of the naïve Bayes score; ranking the groups by fitness, from divergence score; identifying elite groups based on top ranking groups; iteratively performing the following sequence of steps until the fitness measure of the pre-set top ranking number of groups does not substantively change, those steps comprising: selecting parent groups based on fitness, wherein parent groups are cloned elites from a previous iteration; creating children groups, wherein each children is a present iteration variable group combination of two parent groups; mutating children groups, wherein each mutating group comprises a pre-set fraction of children groups with at least one altered parameter value per group; merging the parent groups and children groups; assigning a fitness value of each group based on divergence value; ranking the groups by fitness values; and tagging the top ranking groups as elite groups to be promoted to the next iteration; wherein groups of transaction variables using analogous genetic processes, iteratively valued, scored, and ranked for a particular mathematical score of fitness, progressively maintaining top ranking groups while introducing mutating groups results in an automated process of comprehensively mining the transaction data for predictors of a specified target objective; and outputting said predictors.