What is the main disadvantage of K-means clustering?

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Question: What is the main disadvantage of K-means clustering?

Options:

Correct Answer: It is sensitive to the initial placement of centroids

Solution:

K-means clustering is sensitive to the initial placement of centroids, which can affect the final clustering outcome.

What is the main disadvantage of K-means clustering?

What is the main disadvantage of K-means clustering?

Step 1: Understand that K-means clustering is a method used to group data into clusters.
Step 2: Know that K-means starts by choosing initial points called centroids to represent each cluster.
Step 3: Realize that the position of these centroids can be chosen randomly.
Step 4: Acknowledge that if the centroids are placed poorly at the start, the final clusters may not be accurate.
Step 5: Conclude that this sensitivity to initial placement is the main disadvantage of K-means clustering.

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