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Hierarchical Clustering Q&A
1What is hierarchical clustering?
Answer: Builds nested cluster structure via iterative merging or splitting.
2Agglomerative vs divisive?
Answer: Agglomerative merges bottom-up; divisive splits top-down.
3What is dendrogram?
Answer: Tree diagram showing cluster merge distances and hierarchy.
4How decide clusters from dendrogram?
Answer: Cut the tree at chosen distance threshold.
5What are linkage methods?
Answer: Single, complete, average, and Ward linkage.
6Ward linkage purpose?
Answer: Minimizes increase in within-cluster variance when merging.
7Need scaling?
Answer: Usually yes, due to distance computations.
8Advantages vs K-Means?
Answer: No need to pre-specify k and provides hierarchy insight.
9Limitations?
Answer: Expensive for large datasets and sensitive to noise/outliers.
10One-line summary?
Answer: Hierarchical clustering reveals multi-level structure in data.