Machine Learning Resources

What are some pros and cons of K-Means Clustering?


  • Easy to implement
  • Produces compact-shaped clusters


  • Must specify number of clusters in advance
  • Sensitive to initial choices of centroids
  • Not good at identifying clusters that don’t follow a globular shape

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