How to Get Consistent Classification From Inconsistent LLMs?
A technique for deterministic labeling from stochastic models, with benchmarked Golang implementation.
Summary
K-means clustering is an unsupervised machine learning algorithm that partitions data into K distinct clusters by assigning each point to the nearest cluster centroid and then updating centroids iteratively until convergence.
The method minimizes within-cluster variance and works efficiently for tasks such as customer segmentation, image compression, and anomaly detection. It requires specifying the number of clusters in advance and is sensitive to initial centroid placement and outliers.
