Which hierarchical clustering method begins with all data points in one cluster and splits them into smaller clusters?

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Multiple Choice

Which hierarchical clustering method begins with all data points in one cluster and splits them into smaller clusters?

Explanation:
In hierarchical clustering, there are two main strategies. The approach that begins with all data points in a single cluster and then splits that cluster into smaller ones is divisive clustering. It follows a top-down path: start with one group containing everything, choose a way to split it into two (or more) clusters, then continue splitting the resulting clusters as needed until you reach the desired level of detail. This is exactly the description in the question. The alternative that sounds similar is agglomerative clustering, which works in the opposite direction by starting with each data point as its own cluster and then repeatedly merging the closest clusters. The other option is just the general term for the method (hierarchical clustering), not a specific split-or-merge strategy, and the last option isn’t a clustering method at all.

In hierarchical clustering, there are two main strategies. The approach that begins with all data points in a single cluster and then splits that cluster into smaller ones is divisive clustering. It follows a top-down path: start with one group containing everything, choose a way to split it into two (or more) clusters, then continue splitting the resulting clusters as needed until you reach the desired level of detail. This is exactly the description in the question.

The alternative that sounds similar is agglomerative clustering, which works in the opposite direction by starting with each data point as its own cluster and then repeatedly merging the closest clusters. The other option is just the general term for the method (hierarchical clustering), not a specific split-or-merge strategy, and the last option isn’t a clustering method at all.

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