Which clustering method is more sensitive to outliers?

Which clustering method is more sensitive to outliers?

Step 1: Understand what clustering means. Clustering is a way to group similar items together.
Step 2: Learn about K-means clustering. K-means is a method that groups data by finding the average (mean) of the points in each group.
Step 3: Know what outliers are. Outliers are data points that are very different from the rest of the data.
Step 4: Realize how K-means works. It calculates the center (centroid) of each group using the mean of the points.
Step 5: Understand the impact of outliers. If there is an outlier, it can pull the mean away from the other points, making the centroid inaccurate.
Step 6: Conclude that K-means is sensitive to outliers because they can change the group centers significantly.

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