Clustering of variables relies on relationships among them. The strength of those relationships is generally measured by the correlation coefficients between pairs of variables. This paper proposes specified variable weighted correlation coefficients and takes the clustering around latent variables (CLV) approach as an example to transform the common clustering method into a “weighted” clustering method. The aim is to eliminate factors that are unrelated to the variable that was adopted for weighting to ensure that the cluster centers are sufficiently different and have good correlations with the adopted variable. A log-transformed dataset was used to evaluate the proposed method. Three clusters were obtained under the restriction of the As element, and they represented three ore-controlling factors related to the Goldenville Formation, namely geologic features such as formation, fault contacts, and granitoid intrusions. Not only did the new cluster centers account for most of the variability related to the weighted element (As) but they also showed significant differences in spatial distributions.

变量的聚类依赖于它们之间的关系。这些关系的强度通常通过变量对之间的相关系数来衡量。本文提出了指定的变量加权相关系数，并以围绕潜在变量的聚类（CLV）方法为例，将常见的聚类方法转变为“加权”聚类方法。目的是消除与加权变量无关的因素，以确保聚类中心有足够的差异并与所采用的变量具有良好的相关性。对数转换数据集用于评估所提出的方法。在As元素的限制下获得了三个簇，它们代表了与Goldenville组相关的三个控矿因素，即地质特征，例如地层、断层接触和花岗岩侵入体。新的聚类中心不仅解释了与加权元素 (As) 相关的大部分变异性，而且它们在空间分布上也表现出显着差异。