Divisive hierarchical clustering is a type of clustering algorithm that begins with all data points in a single cluster and progressively splits them into smaller clusters. This method contrasts with agglomerative clustering, where clusters are formed by merging smaller clusters. The process continues until each data point is its own cluster or a stopping criterion is met, allowing for a hierarchical representation of the data.
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