Show pageBacklinksCite current pageExport to PDFBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. 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 (cluster centers or cluster centroid), 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 (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. For instance, better Euclidean solutions can be found using k-medians and k-medoids. k-means_clustering.txt Last modified: 2024/06/07 02:50by 127.0.0.1