Method and apparatus to model the variables of a data set

Apparatus for modeling the variables in an input data set by means of a probabilistic network including data nodes and causal links, said input data set containing statistical data relating to a population group, the variables being personal to customers of a business enterprise, the apparatus comprising:

• data register means for registering the input data set;
• generating means for generating a population of genomes each individually modeling the input data set by means of chromosome data to represent the data nodes in a probabilistic network and the causal links between the data nodes;
• crossover means for performing a crossover operation between the chromosome data of parent genomes in the population to generate offspring genomes;
• adding means for performing an addition operation to add the offspring genomes to the population;
• scoring means for performing a scoring operation on genomes in the population to derive scores representing the correspondence between the genomes and the input data set;
• selecting means for performing a selecting operation to select genomes from the population according to the scores;
• control means for controlling the crossover, scoring, addition and selecting means to repeat their operations for a plurality of generations of the genomes;
• output means for selecting, as an output model, a genome from the last generation;
• and
• means for forming predictions relating to future behavior of said population group.

A method of data mining employing modeling the variables in an input data set to enable the reduction of the knowledge about relationships between the variables by means of a probabilistic network including data nodes and causal links, said input data set containing statistical data relating to at least a portion of a population group, the method comprising the steps of:

• registering the input data set for a plurality of customers of an enterprise;
• generating a population of genomes each individually modeling the input data set by means of chromosome data to represent the data nodes in a probabilistic network and the causal links between the data nodes;
• performing a crossover operation between the chromosome data of parent genomes in the population to generate offspring genomes;
• performing an addition operation to add the offspring genomes to the population;
• performing a scoring operation on genomes in the population to derive scores representing the correspondence between the genomes and the input data set;
• performing a selecting operation to select genomes from the population according to the scores;
• repeating the crossover, scoring, addition and selecting operations for a plurality of generations of the genomes;
• selecting, as an output model, a genome from the last generation;
• and
• forming predictions relating to future behavior of said population group to mine data from the input data set.

Apparatus for data mining employing modeling the variables in an input data set to enable the reduction of the knowledge about relationships between the variables by means of a probabilistic network including data nodes and causal links, said input data set containing statistical data relating to a population group, the apparatus comprising:

• data register means for registering the input data set for a plurality of customers of an enterprise;
• generating means for generating a population of genomes each individually modeling the input data set by means of chromosome data to represent the data nodes in a probabilistic network and the causal links between the data nodes;
• crossover means for performing a crossover operation between the chromosome data of parent genomes in the population to generate offspring genomes;
• adding means for performing an addition operation to add the offspring genomes to the population;
• scoring means for performing a scoring operation on genomes in the population to derive scores representing the correspondence between the genomes and the input data set;
• selecting means for performing a selecting operation to select genomes from the population according to the scores;
• control means for controlling the crossover, scoring, addition and selecting means to repeat their operations for a plurality of generations of the genomes;
• output means for selecting, as an output model, a genome from the last generation;
• and
• means for forming predictions relating to fixture behavior of said population group to mine data from the input data set.