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Which of the following scenarios is K-means clustering NOT suitable for?

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Question: Which of the following scenarios is K-means clustering NOT suitable for?

Options:

  1. When clusters are spherical and evenly sized
  2. When the number of clusters is known
  3. When clusters have varying densities
  4. When outliers are present in the data

Correct Answer: When clusters have varying densities

Solution: 

K-means is not suitable for clusters with varying densities, as it assumes clusters are spherical and of similar size.

Which of the following scenarios is K-means clustering NOT suitable for?

Practice Questions

Q1
Which of the following scenarios is K-means clustering NOT suitable for?
  1. When clusters are spherical and evenly sized
  2. When the number of clusters is known
  3. When clusters have varying densities
  4. When outliers are present in the data

Questions & Step-by-Step Solutions

Which of the following scenarios is K-means clustering NOT suitable for?
Correct Answer: Clusters with varying densities
  • Step 1: Understand what K-means clustering is. It is a method used to group data points into clusters based on their similarities.
  • Step 2: Know that K-means assumes that clusters are spherical in shape and have similar sizes.
  • Step 3: Identify scenarios where clusters may not fit this assumption, such as when clusters have different shapes or densities.
  • Step 4: Conclude that K-means is not suitable for scenarios where clusters vary significantly in density or size.
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