Ward's Method is a hierarchical clustering algorithm that aims to minimize the total within-cluster variance when forming clusters. It does this by merging clusters in a way that results in the least increase in the total sum of squared deviations from the cluster means, making it particularly effective for identifying compact and well-separated clusters.
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