Data Mining Technique for Knowledge Discovery from Engineering Materials Data Sets

Abstract

The goal of this paper is to discuss how data mining technique can be applied in materials informatics to extract knowledge from materials data. Studying material data sets from a data mining perspective can be beneficial for manufacturing and other industrial engineering applications. This work employs an effective materials classification system on design requirements. Experiments were conducted on material datasets that consist of all class of materials. The algorithm of the Naive Bayesian classifier is implemented successively enabling it to solve classification problems and the outcomes can be very useful for design engineers to speed up decision making process in manufacturing and other industrial engineering applications. The comparison of performance with various domains of material classes confirms the advantages of successive learning and suggests its application to other learning domains.