The ClustrKcntrdm.lng Model

k-Centroid Clustering

Given:
a set of points or observations,
in k dimensional feature space,
Classify the points into
a specified number of groups or clusters,
so that points in the same cluster are
close in the k dimensional space.
We minimize the sum over all points, of their distance
from the centroid point of their assigned cluster.
Various distance metrics, such as L1, L2 and higher metrics are allowed.

Keywords:

Classification | Clustering | Data Mining | Grouping |