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Abstract

A neuroagent approach is used in an automated and unified data mining system to provide an explicitly predictive knowledge model. The neuroagent is a neural multi-agent approach based on macro-connectionism and comprises a double integration at the association and symbolic level as well as the knowledge model level. This data mining system permits discovery, evaluation and prediction of the correlative factors of data, i.e., the conjunctions, as corresponding to neuroexpressions (a semantic connection of neuroagents) connected to an output neuroagent which corresponds to the data output, the connection weights yielding the relative significance of these factors to the given output. The system takes data sets called Domains, establishes candidate dimensions or Parameters, categorizes Parameters into discrete bins, and trains a neuroagent network composed of neuroagents allocated for each bin and each output based on a discovery data set, called a Discovery Domain, and by building up the various minimal and contextual neuroexpressions, and setting the appropriate connection weights, the results may therefore be compared with an optional evaluation data set, called an Evaluation Domain to establish the accuracy of the knowledge model, and thereafter applied with some degree of confidence to a prediction set or Prediction Domain. The ranking in importance of the composite Parameters may be calculated as well as the discrimination between the various outputs, which permits the relevant factors of interest to a decision maker to come into focus.

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