K-Means & K-Means++ Clustering
Expectation-Maximization Centroid Partitioning & Probabilistic Seeding
#Clustering#Lloyd Algorithm#K-Means++#Voronoi#Vector Quantization
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STAGE 1 / 7— Anti-Pattern
Section 1: The LLM Anti-Pattern vs Right-Sized Model
The Naive Generative LLM Approach:
Pasting 20,000 customer transaction records into an LLM prompt and asking it to group them into 5 distinct behavioral personas.
Why It Fails in Production:
LLMs exceed context window token limits, hallucinate clusters, cannot enforce convergence, and cost significant money. K-Means runs in 50ms.
Targeted Algorithm (K-Means & K-Means++ Clustering)
Latency:40ms
Cost / 1M Ops:$0.00
Determinism:Convergently Proven Local Minimum
Generative LLM Alternative
Latency:6,000ms
Cost / 1M Ops:$25,000
Determinism:Hallucinated Incoherent Groupings