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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