What is a limitation of using K-Means for clustering?

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Question: What is a limitation of using K-Means for clustering?

Options:

Correct Answer: It assumes clusters are of equal size and density

Solution:

K-Means assumes that clusters are spherical and of similar size and density, which may not be true for all datasets.

What is a limitation of using K-Means for clustering?

What is a limitation of using K-Means for clustering?

Step 1: Understand what K-Means is. K-Means is a method used to group data points into clusters.
Step 2: Know that K-Means tries to create clusters that are round (spherical) in shape.
Step 3: Realize that K-Means also assumes that all clusters are about the same size and density.
Step 4: Recognize that this assumption may not hold true for all datasets, meaning some data may not fit well into these round clusters.
Step 5: Conclude that if the actual data clusters are not spherical or vary in size and density, K-Means may not work effectively.

K-Means Clustering Limitations – K-Means clustering assumes that clusters are spherical and have similar sizes and densities, which can lead to poor performance on datasets with irregularly shaped clusters.