Hierarchical cluster analysis is an important multivariate statistical technique used in informetrics and scientometrics to classify objects into groups or clusters according to their similarity. The objects may be research papers, authors, journals, institutions, keywords, cited references, countries, or research areas. The fundamental purpose of hierarchical cluster analysis is to identify natural groupings within a large body of scholarly or bibliographic data. It is particularly useful for discovering relationships, intellectual structures, research fronts, and patterns of scientific communication. In scientometrics, hierarchical clustering is frequently applied to bibliographic coupling, co-citation analysis, co-word analysis, co-authorship analysis, and citation analysis. For example, if several research papers frequently cite the same set of references, they may belong to a common intellectual cluster. Similarly, keywords that frequently occur together in scientific literature can be grouped to reveal major research themes. Thus, clustering helps transform large and complex bibliometric datasets into meaningful structures