Which clustering method is more sensitive to outliers?

₹0.0

Question: Which clustering method is more sensitive to outliers?

Options:

Correct Answer: K-means clustering

Solution:

K-means clustering is more sensitive to outliers because it uses mean values to determine cluster centroids, which can be skewed by extreme values.

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.

No concepts available.