k-means clustering

Partitioning n observations into k clusters, in which each observation belongs to the cluster with the nearest mean

Start with a set of k means m1 ... mk.

  1. Assign observation to the cluster whose mean point is nearest
  2. Update new means to be the centroids of the observations in the new cluster