Testing the static and dynamic performance of statistical methods for detection of national industrial clusters

This paper proposes a new framework to test the static and dynamic performance of clustering techniques for the detection of national industrial clusters. Principal component analysis (PCA), latent profile analysis (LPA), Louvain community detection algorithm (Louvain), and fuzzy analysis clustering (Fuzzy) are compared. It also proposes aPenalizedDunn Index as a novel metric for assessing the quality of industrial cluster dynamics. The findings indicate that PCA results in large variations in the total number of clusters among countries, but industrial clusters are not consistent over time. LPA and Fuzzy algorithms perform well in the case of static datasets, whereas Louvain offers a good balance between cluster diversity and dynamic consistency.