Hierarchical cluster analysis works very well when dealing with a relatively large number of cases.

Question

BlackTom AI · Accepted Answer

The question presents a statement about hierarchical cluster analysis and asks whether it is true or false in the context of handling a relatively large number of cases.
Option 1: True. Evaluating this claim, we recall that hierarchical clustering, especially agglomerative methods, generally has high computational complexity and memory requirements. As the number of cases grows, the O(n^2) distance calculations and the O(n^3) linkage updates (in the worst cases) become increasingly prohibitive. This makes it impractical for very large datasets. Therefore, asserting that hierarchical clustering works very well with a large number of cases is misleading, since scalability is a known limitation rather than a strength of this approach.
Option 2: False. This option aligns with the practical realities of hierarchical clustering. For large numbers of observations, hierarchical methods tend to be slow and memory-intensive due to pairwise distance computations and repeated merging steps. In contrast, alternative clustering approaches such as k-means, DBSCAN, or mini-batch variants are often preferred for large-scale data because they are designed to be more scalable and efficient. Thus, the statement that hierarchical clustering works very well for a relatively large number of cases does not hold up under typical computational considerations.
In summary, the reasoning above highlights the scalability constraints of hierarchical clustering for large datasets and explains why the claim in the statement is not accurate.

Hierarchical cluster analysis works very well when dealing with a relatively large number of cases.

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