Divisive hierarchical clustering is a method of cluster analysis that starts with all data points in a single cluster and recursively divides it into smaller clusters. This top-down approach contrasts with agglomerative methods, where individual data points are progressively merged into larger clusters. It is particularly useful for spatial clustering and hot spot analysis, as it allows for the identification of hierarchical relationships among data points based on their spatial attributes.
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