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Which distance metric is commonly used in K-means clustering?

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Question: Which distance metric is commonly used in K-means clustering?

Options:

  1. Manhattan distance
  2. Cosine similarity
  3. Euclidean distance
  4. Hamming distance

Correct Answer: Euclidean distance

Solution: 

K-means typically uses Euclidean distance to measure the distance between data points and centroids.

Which distance metric is commonly used in K-means clustering?

Practice Questions

Q1
Which distance metric is commonly used in K-means clustering?
  1. Manhattan distance
  2. Cosine similarity
  3. Euclidean distance
  4. Hamming distance

Questions & Step-by-Step Solutions

Which distance metric is commonly used in K-means clustering?
Correct Answer: Euclidean distance
  • Step 1: Understand that K-means clustering is a method used to group similar data points together.
  • Step 2: Know that in K-means, we need to measure how far apart the data points are from the center of their group, called a centroid.
  • Step 3: The distance between data points and centroids is measured using a formula.
  • Step 4: The most common formula used for this measurement is called Euclidean distance.
  • Step 5: Euclidean distance calculates the straight-line distance between two points in space.
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