Fuzzy Clustering with Spatial Correction and Its Application to Geometallurgical Domaining

Abstract

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Geological domaining at Sungun porphyry copper deposit using cluster analysis

“…In geological domaining, the data set is defined as a set of samples (observations) with several dimensions of attributes that each attribute comprises continuous or categorical variables. By CA, this data set is grouped into different subsets where samples within a group are similar and groups are well separated [9], [22]. One of the conventional CA methods commonly used in clustering techniques and efficiently used for large data is the K-means algorithm.…”

“…The definition of geological domains is the first step in geometallurgical modelling that is vital in taking representative geometallurgical samples [4]- [8]. It is important to note that there is no specific relationship between geological and geometallurgical domains, although geological domains are used to collect geometallurgical representative samples and define their domains [4], [9], [10].…”

Evaluation of safety and deformation characteristics of the secondary stope sandwiched between backfills in underground iron mines

“…Cluster analysis has been widely used in image processing, pattern recognition, and machine learning to classify elements into clusters on the basis of their similarity (Bewley and Upcroft 2013 ; Rodriguez and Laio 2014 ; Dong et al 2018 ; Sepúlveda et al 2018 ). Typical clustering methods included K -means clustering, grid-based clustering, and distribution-based clustering.…”

Clustering of multivariate geostatistical data

“…The Delaunay triangulation is used to define the neighborhood structure of data, and edges lengths are taken into account. Sepúlveda, Dowd, and Xu () proposed a spatially constrained fuzzy clustering approach where the neighborhood structure of data is determined by the k ‐nearest neighbor.…”