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

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It can handle non-spherical clusters
It requires the number of clusters to be specified in advance
It is computationally expensive
It can only be used with numerical data

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

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.

Steps: 4

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.

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

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Questions & Step-by-step Solutions

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