◐ learning · kind algorithm · level 2 · 12h
- Implementa: Unsupervised Learning
- Requiere: Linear Algebra
Partitions points into k clusters by iteratively minimising within-cluster variance. Fast, simple, and sensitive to initialisation and the choice of k.
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 minimizes Euclidean distances. For instance, better Euclidean solutions can be found using k-medians and k-medoids.
Enlaces
- Implementa: Unsupervised Learning
- Requiere: Linear Algebra