Ian W. Flockhart, Nicholas J. Radcliffe
Most data mining systems to date have used variants of traditional machine-learning algorithms to tackle the task of directed knowledge discovery. This paper presents an approach which, as well as being useful for such directed data mining, can also be applied to the further tasks of undirected data mining and hypothesis refinement. This approach exploits parallel genetic algorithms as the search mechanism and seeks to evolve explicit "rules" for maximum comprehensibility. Example rules found in real commercial datasets are presented.