Class to perform KCenter clustering of a given data set
KCenter randomly picks one point from the data, which is now the center of the first cluster. All points are put into the first cluster. In general the furthest point from its center is chosen to be the new center. All points, which are closer to the new center than the old one are assigned to the new cluster. This goes on, until K clusters have been created.
n_clusters (int) – desired number of clusters
>>> cluster = KCenter(n_cluster=200) >>> cluster.fit(data)
Compute the centroids of data.
data (np.ndarray) – A 2D array of data. Columns are features and rows are data examples.