Evaluation of PCA with variable selection for cluster typological domains

Abstract

The modeling of mineral deposits has been improved over the years with the incorporation of mineralogical and metallurgical information obtained from drilling samples that make up the pillars for the construction of resource models. However, sampling data is being made available in large quantities, causing current databases to grow exponentially. The use of machine learning (ML) algorithms has been applied to deal with multidimensional data problems. Principal component analysis (PCA) is a multivariate analysis (MA) technique whose aim is to reduce the d imension of multivariate data. Studies show that results obtained with the reduction of variables were satisfactory in different areas of activity. The purpose of this article is to test variable selection criteria using PCA for geometallurgical data and to check the feasibility of the technique for simplifying variable types and defining typological domains.