Which clustering method requires the selection of a linkage as a factor?

Hierarchical clustering is a method that builds a hierarchy of clusters, and it requires the selection of a linkage criterion to determine how the distances between clusters are calculated. Common linkage criteria include single linkage, complete linkage, average linkage, and Ward’s method. The choice of linkage affects the shape and size of the final clusters, as it influences how the algorithm merges clusters based on the distances between them.

In contrast, K-means clustering does not require the specification of a linkage method. Instead, it partitions the data into a predetermined number of clusters (k) by assigning points to the nearest cluster centroid and then updating the centroids based on the assigned points. This method focuses on minimizing the within-cluster variance and does not involve linking clusters together.

Thus, hierarchical clustering uniquely necessitates the selection of a linkage criterion, making it the correct answer in this context.