Knowledge Discovery with Artificial Neural Networks
Juan R. Rabuñal Dopico (University of A Coruña, Spain), Daniel Rivero Cebrian (University of A Coruña, Spain), Julián Dorado de la Calle (University of A Coruña, Spain) and Nieves Pedreira Souto (University of A Coruña, Spain)
Copyright: © 2005
The world of Data Mining (Cios, Pedrycz & Swiniarrski, 1998) is in constant expansion. New information is obtained from databases thanks to a wide range of techniques, which are all applicable to a determined set of domains and count with a series of advantages and inconveniences. The Artificial Neural Networks (ANNs) technique (Haykin, 1999; McCulloch & Pitts, 1943; Orchad, 1993) allows us to resolve complex problems in many disciplines (classification, clustering, regression, etc.), and presents a series of advantages that convert it into a very powerful technique that is easily adapted to any environment. The main inconvenience of ANNs, however, is that they can not explain what they learn and what reasoning was followed to obtain the outputs. This implies that they can not be used in many environments in which this reasoning is essential.