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Which of the following clustering methods is sensitive to outliers?

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

Options:

  1. K-means
  2. Hierarchical clustering
  3. DBSCAN
  4. Gaussian Mixture Models

Correct Answer: K-means

Solution: 

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

Which of the following clustering methods is sensitive to outliers?

Practice Questions

Q1
Which of the following clustering methods is sensitive to outliers?
  1. K-means
  2. Hierarchical clustering
  3. DBSCAN
  4. Gaussian Mixture Models

Questions & Step-by-Step Solutions

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.
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