Hierarchical clustering should be primarily used for exploration.

The correct answer is True.

Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:

• Agglomerative : This is a “bottom up” approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
• Divisive : This is a “top down” approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.

Hierarchical clustering is often used for exploratory data analysis, as it can provide a useful way to visualize the relationships between data points. However, it is important to note that hierarchical clustering does not provide a unique solution: different algorithms can produce different hierarchies,

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