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

TL;DR

This paper presents a hybrid data mining approach combining Naive Bayesian classification and Pearson correlation for effective knowledge discovery and decision-making in engineering materials design.

Contribution

It introduces a novel hybrid data mining system integrating predictive classification and correlation analysis for engineering materials selection.

Findings

Effective classification of materials achieved

Correlation-based selection improves decision accuracy

Knowledge discovery supports advanced materials design

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.