Which type of hierarchical clustering starts with grouped data points and splits them by hierarchy?

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Multiple Choice

Which type of hierarchical clustering starts with grouped data points and splits them by hierarchy?

Hierarchical clustering can be built in two ways: bottom-up and top-down. The top-down approach, divisive clustering, starts with all data in a single group and then splits that group into smaller subgroups, continuing to split as needed to form a hierarchy. That exact behavior—beginning with one clustered group and dividing it into parts along a hierarchical structure—defines divisive clustering. In contrast, agglomerative clustering does the opposite: it starts with each data point as its own cluster and gradually merges clusters to build the hierarchy. Density-based clustering isn’t inherently about splitting a single group into a hierarchy in the same way; it clusters based on density neighborhoods and isn’t the classic divisive/top-down hierarchical approach.

Hierarchical clustering can be built in two ways: bottom-up and top-down. The top-down approach, divisive clustering, starts with all data in a single group and then splits that group into smaller subgroups, continuing to split as needed to form a hierarchy. That exact behavior—beginning with one clustered group and dividing it into parts along a hierarchical structure—defines divisive clustering.

In contrast, agglomerative clustering does the opposite: it starts with each data point as its own cluster and gradually merges clusters to build the hierarchy. Density-based clustering isn’t inherently about splitting a single group into a hierarchy in the same way; it clusters based on density neighborhoods and isn’t the classic divisive/top-down hierarchical approach.

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