What is a potential drawback of using K-means clustering?

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Question: What is a potential drawback of using K-means clustering?

Options:

Correct Answer: It requires the number of clusters to be specified in advance

Solution:

A potential drawback of K-means clustering is that it requires the number of clusters to be specified in advance, which can be challenging.

What is a potential drawback of using K-means clustering?

What is a potential drawback of using K-means clustering?

Step 1: Understand what K-means clustering is. It is a method used to group data into clusters based on similarities.
Step 2: Know that K-means requires you to decide how many clusters (groups) you want to create before starting.
Step 3: Realize that choosing the right number of clusters can be difficult because you may not know how many groups are in your data.
Step 4: A potential drawback is that if you choose the wrong number of clusters, the results may not be meaningful or useful.

K-means Clustering – A method of partitioning data into K distinct clusters based on feature similarity.
Cluster Number Specification – The requirement to define the number of clusters (K) before running the algorithm, which can lead to suboptimal clustering if not chosen correctly.