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What is a common initialization method for K-means clustering?

Practice Questions

Q1
What is a common initialization method for K-means clustering?
  1. Randomly selecting data points as initial centroids
  2. Using the mean of the dataset as the centroid
  3. Hierarchical clustering to determine initial centroids
  4. Using the median of the dataset as the centroid

Questions & Step-by-Step Solutions

What is a common initialization method for K-means clustering?
  • Step 1: Understand that K-means clustering is a method used to group data points into clusters.
  • Step 2: Know that each cluster has a center point called a centroid.
  • Step 3: To start the K-means algorithm, we need to choose initial positions for these centroids.
  • Step 4: A common way to choose these initial centroids is to randomly select a few data points from the dataset.
  • Step 5: These randomly selected data points will be the starting points for the centroids in the K-means algorithm.
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