Which of the following clustering methods is sensitive to outliers?

Which of the following clustering methods is sensitive to outliers?

Correct Answer: K-means

Step 1: Understand what clustering methods are. Clustering methods group similar data points together.
Step 2: Learn about K-means clustering. K-means is a method that finds the center (centroid) of a group of data points.
Step 3: Know what outliers are. Outliers are data points that are very different from others in the group.
Step 4: Realize how K-means works. It calculates the average position of data points to find the centroid.
Step 5: Understand the impact of outliers. If an outlier is present, it can pull the centroid away from the main group of data points.
Step 6: Conclude that K-means is sensitive to outliers because they can change the centroid's position, leading to incorrect clustering.

No concepts available.