Which factor is most directly affected by outliers in K-means centroid calculations?

Which factor is most directly affected by outliers in K-means centroid calculations?

• A. Cluster Count
• B. Sensitivity to Outliers
• C. Dimensionality
• D. Initialization Speed

Answer: (B)

The central idea here is how a K-means centroid is determined. In K-means, each cluster’s centroid is the average of the points assigned to that cluster. Outliers are extreme values that sit far from the main group, so they pull the average toward them. That means the centroid’s position can be shifted noticeably by a few outliers, which in turn changes which points are assigned to which clusters and can distort the entire clustering result. The direct consequence is the method’s sensitivity to outliers—the centroid calculations react strongly when outliers are present. The other factors aren’t driven as directly by outliers. The number of clusters is a user-chosen parameter, not something changed by data outliers. Dimensionality refers to how many features the data have, which outliers don’t inherently alter. Initialization speed concerns how the starting positions are chosen and how fast the algorithm converges, which is more about algorithmic setup than the data’s extreme values. To mitigate this sensitivity, you can preprocess to remove or cap outliers, or use robust alternatives like K-medoids or algorithms that minimize different distance measures.

The central idea here is how a K-means centroid is determined. In K-means, each cluster’s centroid is the average of the points assigned to that cluster. Outliers are extreme values that sit far from the main group, so they pull the average toward them. That means the centroid’s position can be shifted noticeably by a few outliers, which in turn changes which points are assigned to which clusters and can distort the entire clustering result. The direct consequence is the method’s sensitivity to outliers—the centroid calculations react strongly when outliers are present.

The other factors aren’t driven as directly by outliers. The number of clusters is a user-chosen parameter, not something changed by data outliers. Dimensionality refers to how many features the data have, which outliers don’t inherently alter. Initialization speed concerns how the starting positions are chosen and how fast the algorithm converges, which is more about algorithmic setup than the data’s extreme values. To mitigate this sensitivity, you can preprocess to remove or cap outliers, or use robust alternatives like K-medoids or algorithms that minimize different distance measures.