K-Means Clustering
An unsupervised learning algorithm that partitions data into k clusters by minimizing within-cluster variance.
In more detail
K-Means iteratively assigns points to nearest centroid and updates centroids. Simple and scalable but requires specifying k and is sensitive to initialization. Useful for customer segmentation and exploratory analysis.
Examples
- Customer segmentation
- Document clustering
- Image segmentation
Related terms
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