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What is a key advantage of hierarchical clustering over K-means?

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Question: What is a key advantage of hierarchical clustering over K-means?

Options:

  1. It requires fewer computations
  2. It does not require the number of clusters to be specified in advance
  3. It is always more accurate
  4. It can only handle small datasets

Correct Answer: It does not require the number of clusters to be specified in advance

Solution: 

A key advantage of hierarchical clustering is that it does not require the number of clusters to be specified in advance, allowing for more flexibility.

What is a key advantage of hierarchical clustering over K-means?

Practice Questions

Q1
What is a key advantage of hierarchical clustering over K-means?
  1. It requires fewer computations
  2. It does not require the number of clusters to be specified in advance
  3. It is always more accurate
  4. It can only handle small datasets

Questions & Step-by-Step Solutions

What is a key advantage of hierarchical clustering over K-means?
  • Step 1: Understand that clustering is a way to group similar items together.
  • Step 2: Know that K-means is a clustering method that requires you to decide how many groups (clusters) you want before starting.
  • Step 3: Realize that hierarchical clustering does not need you to decide the number of clusters beforehand.
  • Step 4: Recognize that this flexibility in hierarchical clustering allows you to explore different groupings of the data.
  • Step 5: Conclude that the ability to not specify the number of clusters in advance is a key advantage of hierarchical clustering.
  • Hierarchical Clustering – A clustering method that builds a hierarchy of clusters without needing to specify the number of clusters beforehand.
  • K-means Clustering – A clustering method that requires the user to define the number of clusters before running the algorithm.
  • Flexibility in Clustering – The ability to adapt the number of clusters based on the data structure rather than a predetermined number.
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