Regarding hierarchical clustering, which statement is accurate?

In hierarchical clustering, the process typically involves joining or splitting data points based on their similarities, and it is characterized by a tree-like structure known as a dendrogram. The correct statement reflects that hierarchical clustering builds a hierarchy of clusters through a method known as agglomerative clustering or divisive clustering.

In the agglomerative approach, the algorithm starts with each individual observation as its own cluster—these can later be merged into larger clusters based on distance metrics. In contrast, if using a divisive method, it begins with all observations in a single cluster and recursively splits them into smaller clusters. Thus, while the wording of the correct answer alludes to a method primarily characterized by recursive splitting, it can also apply to merging in a hierarchical framework led by the context of hierarchical clustering methods. Overall, it encapsulates the nature of hierarchical approaches effectively by emphasizing the systematic organization of observations into clusters through recursive techniques.

Other statements misunderstand the fundamental characteristics of hierarchical clustering. For instance, while identifying outliers can occur during analysis, hierarchical clustering does not inherently excel at this task. Similarly, it is less efficient compared to k-means, which tends to perform better with larger datasets. Lastly, the statement regarding clusters being initially single observations pertains to the ag