A data mining based approach for improving the quality of engineering graduates

Data mining, or Knowledge Discovery from very large Databases (KDD), is the extraction of previously unknown and potentially useful information from the data stored in large databases. The association rule discovery method is also analysed. The paper describes a data mining application in large databases of engineering student records. The authors' goal has been to discover knowledge that is useful in improving the quality of engineering graduates. The knowledge discovered can be used for assisting the development of new curricula and the improvement of existing curricula, and provides an important and valuable option against the traditional administration approach based on experience and assumption. The methods shown in this paper can also assist students in selecting the appropriate major according to their profile through the utilisation of a decision tree. In this paper, the authors present the steps made to reach these goals and discuss the problems encountered to extract new and useful knowledge during the discovery process.