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What is a limitation of using K-Means for clustering?
Practice Questions
Q1
What is a limitation of using K-Means for clustering?
It can only cluster numerical data
It assumes clusters are of equal size and density
It is not scalable to large datasets
It requires a distance metric
Questions & Step-by-Step Solutions
What is a limitation of using K-Means for clustering?
Steps
Concepts
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.
No concepts available.
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