K-Means Clustering

📘 Artificial Intelligence & Machine Learning Basics 👁 59 views 📅 Nov 05, 2025
⏱ Estimated reading time: 1 min

Definition:
An unsupervised learning algorithm that groups data into K clusters based on similarity.

Key Points:

  • Each cluster has a centroid

  • Points are assigned to the cluster with the nearest centroid

  • Iteratively updates centroids until clusters stabilize

Applications:

  • Customer segmentation

  • Image compression

  • Market basket analysis

Advantages:

  • Simple and fast

  • Works well for large datasets

Limitations:

  • Requires predefined K

  • Sensitive to outliers

  • Not suitable for non-globular clusters


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