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?
It is sensitive to outliers
It requires the number of clusters to be specified in advance
It can converge to local minima
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.
