Data mining use for learning process design of an information source locator agent

The aim of this work is to present a data mining application to software engineering. We describe the use of data mining in some parts of the design process of a dynamic decision support system agent-based architecture. The main function of this system is to guide information requirements from users to the domains that offer greater possibilities of answering them. For that purpose, a strategy is developed, which provides the system with capacity for analyzing an information requirement, and determining to which domains it will be directed. To learn from errors made during its operation, a learning mechanism based in CBR techniques is also proposed. On the one hand, by using data mining techniques it is possible to define a discriminating function to classify the system domains into two groups: those that can probably provide an answer to the information requirement made to the system, and those that cannot. On the other hand, the application of data mining to the cases base allows the specification of rules to settle relationships among the stored cases with the aim of inferring possible causes of error in the domains classification. In this way, a learning mechanism is designed to update the knowledge base and thus improve the already made classification as regards the values assigned to the discriminating function. Eje: Aprendizaje y reconocimiento de patrones Red de Universidades con Carreras en Informática (RedUNCI)