K-means clustering
id:
k-means-clustering-167-6228214
title:
K-means clustering
text:
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, serving as a prototype of the cluster. 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,
brand slug:
wiki
category slug:
encyclopedia
description:
Vector quantization algorithm minimizing the sum of squared deviations
original url:
https://en.wikipedia.org/wiki/K-means_clustering
date created:
2005-05-09T00:06:48Z
date modified:
2024-08-30T07:03:26Z
main entity:
{"identifier":"Q310401","url":"https://www.wikidata.org/entity/Q310401"}
image:
fields total:
13
integrity:
15