Classical clustering (CC) normally divides a database into a predetermined number of fixed clusters before assigning each target site into one of these clusters to build a quasi-local model. Because the range of target sites is much larger than the range of clusters, it is possible that the identified cluster contains database sites that are weakly correlated to some assigned target sites, especially when these target sites are located near the cluster boundaries. The existence of weakly correlated sites in the cluster may make the quasi-local model less effective in predicting soil properties at the target site. This paper proposes a Tailored Clustering Enabled Regionalization (TCER) framework that can minimize the inference uncertainty at a target site. It mainly consists of two parts: (1) a site similarity measure to quantify the similarity between database sites and the target site and (2) a novel tailored clustering (TC) approach to identify the optimal cluster (called quasi-regional cluster) of database sites with the highest site similarity measures. In comparison to the traditional TC, the novel TC does not require users to specify a site similarity threshold that divides database sites into those within the cluster and those outside the cluster. Finally, a hierarchical Bayesian model is applied to the quasi-regional cluster (rather than the entire database) to infer geotechnical/geological properties at the target site. The capability of TCER is verified using synthetic and real examples. It is shown that the proposed TCER is computationally efficient and can reduce inference uncertainty.

中文翻译：

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定制聚类方法来识别准区域站点

经典聚类（CC）通常将数据库划分为预定数量的固定簇，然后将每个目标站点分配到这些簇之一以构建准局部模型。由于目标站点的范围远大于群集的范围，因此所识别的群集可能包含与某些分配的目标站点弱相关的数据库站点，特别是当这些目标站点位于群集边界附近时。簇中弱相关站点的存在可能会使准局部模型在预测目标站点的土壤特性方面效果较差。本文提出了一种定制聚类启用区域化（TCER）框架，可以最大限度地减少目标站点的推理不确定性。它主要由两部分组成：（1）站点相似性度量，用于量化数据库站点与目标站点之间的相似性；（2）新颖的定制聚类（TC）方法，用于识别最佳聚类（称为准区域聚类）具有最高站点相似性度量的数据库站点。与传统的TC相比，新颖的TC不需要用户指定将数据库站点分为集群内站点和集群外站点的站点相似度阈值。最后，将分层贝叶斯模型应用于准区域集群（而不是整个数据库），以推断目标地点的岩土/地质特性。使用合成和真实示例验证了 TCER 的能力。结果表明，所提出的 TCER 计算效率高，并且可以减少推理不确定性。