Data mining technique with maintenance of fitness history

A client computer system for a data mining system, for use with a data mining training database containing a plurality of data samples, comprising:

• a processing subsystem;
• a memory having a candidate gene database identifying a client-centric pool of candidate individuals, each of the candidate individuals identifying a plurality of conditions and at least one corresponding proposed output in dependence upon the conditions,
• a client gene pool processor which:
• tests each individual in a testing subset of at least one of the candidate individuals, each of the tests applying the conditions of the respective individual to a respective subset of the data samples in the training database to propose a result, each individual in the testing subset being tested on at least one data sample, the testing being performed in a manner that precludes testing each given individual in the testing subset from applying the conditions of the respective individual to any data sample already identified in the first history associated with the given individual;
• stores, in association with each of the candidate individuals in the testing subset, a respective first history identifying the data samples on which the respective individual has been tested, at least so far as is known by the computer system;
• and
• calculates an client-centric overall fitness estimate for each of the individuals in the testing subset, in dependence upon the results proposed by the respective individual when the conditions of the respective individual were applied to each of the data samples indicated in the first history associated with the respective individual,
• the client computer system further including a competition module which selects individuals for discarding in dependence upon comparisons among their respective client-centric overall fitness estimates;
• and
• the client computer system further including a gene harvesting module which forwards to a central server infrastructure for potential deployment or further testing, selected ones of the remaining individuals from the client-centric pool of candidate individuals.

A computer-implemented data mining method, for use with a data mining training database containing a plurality of data samples, comprising the steps of:

• providing a computer system having a memory having a candidate gene database identifying a pool of candidate individuals, each of the candidate individuals identifying a plurality of conditions and at least one corresponding proposed output in dependence upon the conditions,
• the computer system testing each individual in a testing subset of at least one of the candidate individuals, each of the tests applying the conditions of the respective individual to a respective subset of the data samples in the training database to propose a result, each individual in the testing subset being tested on at least one data sample and at least one of the individuals in the testing subset being tested on more than one data sample;
• storing, in association with each of the candidate individuals in the testing subset, a respective first history identifying the data samples on which the respective individual has been tested;
• calculating an overall fitness estimate for each of the individuals in the testing subset, in dependence upon the results proposed by the respective individual when the conditions of the respective individual were applied to each of the data samples indicated in the first history associated with the respective individual;
• the computer system selecting individuals for discarding in dependence upon comparisons among their respective overall fitness estimates;
• and
• providing for deployment of selected ones of the remaining individuals from the pool of candidate individuals.

A computer-implemented data mining method, for use with a data mining training database containing a plurality of data samples, comprising the steps of:

• providing a computer system having a memory having a candidate gene database identifying a pool of candidate individuals, each of the candidate individuals identifying a plurality of conditions and at least one corresponding proposed output in dependence upon the conditions,
• the computer system testing each individual in a testing subset of at least one of the candidate individuals, each of the tests applying the conditions of the respective individual to a respective subset of the data samples in the training database to propose a result, each individual in the testing subset being tested on at least one data sample, the testing being performed in a manner that precludes testing each given individual in the testing subset from applying the conditions of the respective individual to any data sample already identified in the first history associated with the given individual;
• storing, in association with each of the candidate individuals in the testing subset, a respective first history identifying the data samples on which the respective individual has been tested, at least so far as is known by the computer system;
• calculating an overall computer system-centric fitness estimate for each of the individuals in the testing subset, in dependence upon the results proposed by the respective individual when the conditions of the respective individual were applied to each of the data samples indicated in the first history associated with the respective individual;
• the computer system selecting individuals for discarding in dependence upon comparisons among their respective overall fitness estimates;
• and
• forwarding to a central server infrastructure for potential deployment or further testing, selected ones of the remaining individuals from the pool of candidate individuals.