What is the main difference between K-means and K-medoids?

Practice Questions

Q1
What is the main difference between K-means and K-medoids?
  1. K-means uses centroids, while K-medoids uses actual data points
  2. K-medoids is faster than K-means
  3. K-means can handle categorical data, while K-medoids cannot
  4. There is no difference; they are the same algorithm

Questions & Step-by-Step Solutions

What is the main difference between K-means and K-medoids?
  • Step 1: Understand that both K-means and K-medoids are clustering algorithms used to group data points.
  • Step 2: Learn that K-means calculates the center of a cluster, called a centroid, which is the average of all points in that cluster.
  • Step 3: Realize that K-medoids, on the other hand, chooses an actual data point from the cluster to represent it, called a medoid.
  • Step 4: Note that K-means can be affected by outliers because it uses the average, while K-medoids is more robust to outliers since it uses actual data points.
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