Which of the following clustering methods is sensitive to outliers?

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K-means
Hierarchical clustering
DBSCAN
Gaussian Mixture Models

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Q: Which of the following clustering methods is sensitive to outliers?

Solution: K-means is sensitive to outliers because they can significantly affect the position of the centroid, leading to poor clustering results.

Steps: 6

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.

Which of the following clustering methods is sensitive to outliers?

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