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What is a potential drawback of K-Means clustering?
What is a potential drawback of K-Means clustering?
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Practice Questions
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What is a potential drawback of K-Means clustering?
It can handle non-linear data well
It requires the number of clusters to be specified in advance
It is computationally inexpensive
It is robust to outliers
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K-Means requires the number of clusters to be specified beforehand, which can be a limitation.
Questions & Step-by-step Solutions
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Q
Q: What is a potential drawback of K-Means clustering?
Solution:
K-Means requires the number of clusters to be specified beforehand, which can be a limitation.
Steps: 4
Show Steps
Step 1: Understand that K-Means is a method used to group data into clusters.
Step 2: Know that when using K-Means, you have to decide how many clusters you want before starting.
Step 3: Realize that if you choose the wrong number of clusters, the results may not be accurate or useful.
Step 4: Recognize that this requirement to specify the number of clusters can be a limitation of K-Means.
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