Hybrid Data Mining Technique for Knowledge Discovery from Engineering Materials' Data sets

Abstract

Studying materials informatics from a data mining perspective can be beneficial for manufacturing and other industrial engineering applications. Predictive data mining technique and machine learning algorithm are combined to design a knowledge discovery system for the selection of engineering materials that meet the design specifications. Predictive method-Naive Bayesian classifier and Machine learning Algorithm - Pearson correlation coefficient method were implemented respectively for materials classification and selection. The knowledge extracted from the engineering materials data sets is proposed for effective decision making in advanced engineering materials design applications.

12 pages, 8 figures; International Journal of Database Management Systems (IJDMS), Vol.3, No.1, February 2011. arXiv admin note: text overlap with arXiv:1206.3078 by other authors