Which clustering method is more sensitive to outliers?
Practice Questions
1 question
Q1
Which clustering method is more sensitive to outliers?
K-means clustering
Hierarchical clustering
Both are equally sensitive
Neither is sensitive to outliers
K-means clustering is more sensitive to outliers because it uses mean values to determine cluster centroids, which can be skewed by extreme values.
Questions & Step-by-step Solutions
1 item
Q
Q: Which clustering method is more sensitive to outliers?
Solution: K-means clustering is more sensitive to outliers because it uses mean values to determine cluster centroids, which can be skewed by extreme values.
