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What are some of the pros and cons of hierarchical clustering compared to K-Means?

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  • Do not have to specify the number of clusters before running the algorithm
  • Results are reproducible and not subject to randomness introduced by choice of initial centroids as in K-Means


  • Requires computation of pairwise linkage matrix, which can be computationally expensive
  • Results can differ based on the linkage criteria used
  • Can be sensitive to noise in data

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