What are the strengths and weaknesses of using the Ward's method in Hierarchical Clustering?
- Maximizes mean distance but sensitive to initial configuration
- Maximizes variance but creates well-separated clusters
- Minimizes mean distance but less compact clusters
- Minimizes variance but sensitive to outliers
Ward's method in Hierarchical Clustering aims to minimize the variance within clusters, leading to tightly packed clusters. Strength: It often results in compact and balanced clusters. Weakness: It can be sensitive to outliers, as it minimizes the total within-cluster variance, which can be disproportionately influenced by extreme values.
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