Dynamic time series clustering with multivariate linkage and automatic dendrogram cutting using a recursive partitioning algorithm

The number of partitions identified in a cluster analysis is traditionally a critical point of the procedure. There are many solutions available in the literature that researchers can exploit to guide how they determine the number of clusters. However, when a statistical analysis requires repeated cluster analyses, such as when tracking the changing composition of clusters over time, an automated approach can be beneficial. We propose a method to automatically cut dendrograms generated by a hierarchical clustering technique using a novel algorithm called Model-Based Recursive Partitioning. As a case study, the method is applied to dynamically analyze the interdependencies between industry sectors during the pandemic period.