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Which of the following is a disadvantage of K-means clustering?

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Question: Which of the following is a disadvantage of K-means clustering?

Options:

  1. It is sensitive to outliers
  2. It requires the number of clusters to be specified in advance
  3. It can converge to local minima
  4. All of the above

Correct Answer: All of the above

Solution: 

All of the listed options are disadvantages of K-means clustering, making it sensitive to outliers, requiring prior knowledge of the number of clusters, and potentially converging to local minima.

Which of the following is a disadvantage of K-means clustering?

Practice Questions

Q1
Which of the following is a disadvantage of K-means clustering?
  1. It is sensitive to outliers
  2. It requires the number of clusters to be specified in advance
  3. It can converge to local minima
  4. All of the above

Questions & Step-by-Step Solutions

Which of the following is a disadvantage of K-means clustering?
Correct Answer: All of the above
  • Step 1: Understand what K-means clustering is. It is a method used to group data into clusters based on similarities.
  • Step 2: Identify the disadvantages of K-means clustering. These include:
  • Step 3: Recognize that K-means is sensitive to outliers, meaning that extreme values can affect the results.
  • Step 4: Note that K-means requires prior knowledge of the number of clusters, which means you need to decide how many groups to create before running the algorithm.
  • Step 5: Understand that K-means can converge to local minima, which means it might find a solution that is not the best possible one.
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