Question: What is a limitation of K-means clustering?
Options:
It can only handle numerical data
It requires the number of clusters to be specified in advance
It is sensitive to outliers
All of the above
Correct Answer: All of the above
Solution:
K-means clustering has several limitations, including the need to specify the number of clusters and sensitivity to outliers.
What is a limitation of K-means clustering?
Practice Questions
Q1
What is a limitation of K-means clustering?
It can only handle numerical data
It requires the number of clusters to be specified in advance
It is sensitive to outliers
All of the above
Questions & Step-by-Step Solutions
What is a limitation of K-means clustering?
Step 1: Understand that K-means clustering is a method used to group data into clusters.
Step 2: Know that one limitation is that you have to decide how many clusters (groups) you want before starting.
Step 3: Realize that if you choose the wrong number of clusters, the results may not be good.
Step 4: Learn that K-means is also sensitive to outliers, which are data points that are very different from others.
Step 5: Understand that if there are outliers, they can affect the position of the clusters and lead to inaccurate results.
K-means Clustering Limitations – K-means clustering requires the user to predefine the number of clusters, which can lead to suboptimal results if the true number of clusters is unknown. Additionally, it is sensitive to outliers, which can skew the results.
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