A home made implementation of the naive K means clustering algorithm.
The algorithm automatically fits K clusters in a numpy dataset with points allocated to the cluster whose centroid is closest in distance.
The algorithm so far only uses Euclidian distance.
Contact matthieuglotz@gmail.com for any questions or comments on this file.