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.”

IS 4487-006 Fall 2025 Final Exam December 10 from 10:30 to 12:30

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

View Explanation

Log in for full answers

Similar Questions

Which of the following statements about clustering in machine learning is true?

In k-means clustering, what is the primary goal of the algorithm? (you can choose more than one)

The elbow plot helps determine the optimal number of clusters by looking for:

What does clustering accomplish?

Which method is best for detecting patterns in unlabeled data?

Which of the descriptions listed below is TRUE?

What is the optimal number of clusters based on the following results?

Which of the following statements about clustering are correct?

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler