K-Means

Definitions

1. A clustering algorithm that partitions data into k groups by repeatedly updating centroids. It influences how models are trained, evaluated, or served, and it can materially change accuracy, robustness, latency, cost, or interpretability. Practitioners usually track it alongside data quality, compute limits, and validation results when moving models into production.

   In plain English: K-Means is an AI concept that affects how a model learns, predicts, or gets deployed. It matters because it changes quality, speed, or reliability.

   Example: "We revisited K-Means during model evaluation because the first run looked fine offline but behaved poorly in production, and the adjustment improved quality without breaking our latency or compute budget."

Related Terms

• Backtracking
• Big-O
• Binary Tree
• Graph
• Hash Table
• Heap
• Reduction
• Gaussian Mixture Model
• Import Hash