Your team experiments with increasing K in K-Means from 3 to 8. What is the expected effect?

Question

BlackTom AI · Accepted Answer

When you increase K in K-Means from 3 to 8, you are creating more clusters to assign data points to. This naturally leads to each cluster containing fewer points on average, which means the clusters become smaller in size.

Option by option:
- Greater drift: This is unlikely. Drift refers to the movement of centroids during iterations; increasing K typically stabilizes assignments within smaller, more localized groups rather than causing more drift across the entire solution.
- Lower variance: While it’s true that increasing K tends to reduce within-cluster variance (since data points are grouped into tighter clusters), the question asks for the expected effect in plain terms of cluster structure. The most direct, descriptive consequence of more clusters is that clusters themselves are smaller. The option “Lower variance” is technically related but less direct as the primary observable effect when you increase K. 
- Smaller clusters: This is the most straightforward consequence of having more clusters to assign data points to. Each cluster covers a smaller region of the data space, leading to smaller, more granular groupings.
- Fewer iterations: Increasing K does not inherently reduce the number of iterations required for convergence; convergence behavior depends on data geometry and initialization, not simply on the value of K.

In summary, as K grows from 3 to 8, the most evident structural change is that the clusters become smaller and more numerous, which aligns with selecting “Smaller clusters.”

Your team experiments with increasing K in K-Means from 3 to 8. What is the expected effect?单项选择题

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