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

2012/09/19 by Doreswamy, Hemanth K. S, S, Hemanth K.
Computer Science · Engineering · #Databases (cs.DB) #FOS: Computer and information sciences #Fault Detection and Control Systems #Neural Networks and Applications #Rough Sets and Fuzzy Logic #cs.DB

paper · pdf · doi:10.48550/arxiv.1209.4169

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

arxiv created 2012/09/19 · openalex publication_date 2012/09/19 · arxiv updated 2012/09/20 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

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.

Citations

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