Division-seeking algorithms

Type: Concept

As of November 2026, Division-seeking algorithms has been mentioned in one podcast episode on The Diary Of A CEO (Nov 2025).

k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances, but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minim