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In which scenario would you prefer hierarchical clustering over K-means?

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Question: In which scenario would you prefer hierarchical clustering over K-means?

Options:

  1. When the number of clusters is known
  2. When the dataset is very large
  3. When you need a visual representation of the clustering process
  4. When clusters are expected to be spherical

Correct Answer: When you need a visual representation of the clustering process

Solution: 

Hierarchical clustering is preferred when a visual representation of the clustering process is needed, as it provides a dendrogram.

In which scenario would you prefer hierarchical clustering over K-means?

Practice Questions

Q1
In which scenario would you prefer hierarchical clustering over K-means?
  1. When the number of clusters is known
  2. When the dataset is very large
  3. When you need a visual representation of the clustering process
  4. When clusters are expected to be spherical

Questions & Step-by-Step Solutions

In which scenario would you prefer hierarchical clustering over K-means?
  • Hierarchical Clustering – A clustering method that builds a hierarchy of clusters, often visualized with a dendrogram.
  • K-means Clustering – A partitioning method that divides data into a specified number of clusters based on distance to centroids.
  • Dendrogram – A tree-like diagram that records the sequences of merges or splits in hierarchical clustering.
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