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K-Means Clustering is a medium coding interview question covering machine learning, math — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.
Implement the K-Means clustering algorithm.
Given data points and k, find k cluster centroids by: 1. Initialize centroids randomly (or use k-means++ selection) 2. Repeat until convergence: a. Assign each point to nearest centroid b. Update centroid = mean of assigned points
Example: points=[[1,1],[2,1],[8,8],[9,8]], k=2 -> clusters {0,1} and {2,3}
Return final centroids and cluster assignments.
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