Regarding hierarchical clustering, which statement is true?

In hierarchical clustering, the core process involves building clusters in a stepwise manner, where at each stage, the algorithm evaluates the pairwise dissimilarities between clusters. When two clusters are merged, the distance (or dissimilarity) between them is determined based on the chosen linkage criterion (e.g., single-linkage, complete-linkage, average-linkage). This step is crucial for understanding how clusters are formed and allows the algorithm to decide which two clusters to combine next based on their proximity.

Choosing this statement highlights the fundamental mechanism of how hierarchical clustering operates. It emphasizes the importance of comparing inter-cluster dissimilarities at every fusion step, which shapes the entire structure of the resulting dendrogram—a visual representation of the clustering process.

The other options present misunderstandings about the principles of hierarchical clustering; for example, standardization, while often beneficial in clustering to treat all variables equally, is not a strict requirement for the technique to function. Similarly, the nature of hierarchical clustering means that running it multiple times can yield different clusters due to the underlying dissimilarity measures, or due to variations in the data. Lastly, the order in which clusters are combined will influence the overall dissimilarity results. This shows that order has a significant impact on