Which of the following is a limitation of K-Means clustering?

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Question: Which of the following is a limitation of K-Means clustering?

Options:

Correct Answer: It is sensitive to outliers

Solution:

K-Means is sensitive to outliers, which can skew the results and affect cluster centroids.

Which of the following is a limitation of K-Means clustering?

Which of the following is a limitation of K-Means clustering?

Step 1: Understand what K-Means clustering is. It is a method used to group data points into clusters based on their similarities.
Step 2: Learn about outliers. Outliers are data points that are very different from the rest of the data.
Step 3: Recognize that K-Means calculates the center of each cluster, called the centroid.
Step 4: Realize that if there are outliers in the data, they can pull the centroid away from the main group of data points.
Step 5: Understand that this can lead to incorrect clustering results, as the clusters may not represent the true structure of the data.

No concepts available.