Which of the following is a disadvantage of K-means clustering?
Practice Questions
1 question
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
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.
Questions & Step-by-step Solutions
1 item
Q
Q: Which of the following is a disadvantage of K-means clustering?
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.
Steps: 5
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.
