Statistics For Risk Modeling (SRM) Qualitative Practice Test

Hierarchical clustering indeed requires the selection of a linkage method, which defines how the distance between clusters is calculated. Linkage methods can vary; common options include single linkage, complete linkage, average linkage, and ward's linkage. Each method influences the final structure of the dendrogram and the clusters formed, as they each define how to measure the distance between clusters differently. In contrast, K-means clustering does not involve choosing a linkage method but rather focuses on centroids and partitions the data into a predetermined number of clusters based on minimizing the within-cluster variance. Choosing a linkage is essential in hierarchical clustering because it affects how clusters are combined or split and ultimately determines the outcome of the clustering process. Therefore, the statement accurately reflects a fundamental characteristic of hierarchical clustering compared to K-means.