Other clustering:

The ``K-center'' -algorithm focuses on a radically different approach. Instead of dividing the data into similarity groups it tries to separate a group of K data-points from the rest of the data (2 clusters). These K records are chosen as the most representative records within the data, which means that the distance from any record to its closest ``representative'' is minimized. The resulting K centers then can be used for showing the diversity of the data. Also representative neurons for Radial Basis Functions can be created using this algorithm.