K-Means Clustering
📘 Artificial Intelligence & Machine Learning Basics
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📅 Nov 05, 2025
⏱ Estimated reading time: 1 min
Definition:
An unsupervised learning algorithm that groups data into K clusters based on similarity.
Key Points:
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Each cluster has a centroid
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Points are assigned to the cluster with the nearest centroid
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Iteratively updates centroids until clusters stabilize
Applications:
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Customer segmentation
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Image compression
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Market basket analysis
Advantages:
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Simple and fast
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Works well for large datasets
Limitations:
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Requires predefined K
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Sensitive to outliers
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Not suitable for non-globular clusters
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