In K-Means, the final clusters depend heavily on:

Subject: Machine Learning

Topic: K-Means & Clustering in Unsupervised Learning

Options:

1. Option A: The initial centroid selection.
2. Option B: The distance metric used.
3. Option C: The number of iterations.
4. Option D: The support threshold.

Correct Answer:

Option A: The initial centroid selection.

Explanation:

K-Means is sensitive to initialization; different starting centroids can lead to different local minima and clustering results.