algorithms.clustering.utils

Module: algorithms.clustering.utils

Functions

nipy.algorithms.clustering.utils.kmeans(X, nbclusters=2, Labels=None, maxiter=300, delta=0.0001, verbose=0, ninit=1)

kmeans clustering algorithm

Parameters:

X: array of shape (n,p): n = number of items, p = dimension :

data array

nbclusters (int), the number of desired clusters :

Labels = None array of shape (n) prior Labels. :

if None or inadequate a random initilization is performed.

maxiter=300 (int), the maximum number of iterations before convergence :

delta: float, optional, :

the relative increment in the results before declaring convergence.

verbose: verbosity mode, optionall :

ninit: int, optional, number of random initalizations :

Returns:

Centers: array of shape (nbclusters, p), :

the centroids of the resulting clusters

Labels : array of size n, the discrete labels of the input items

J (float): the final value of the inertia criterion :

nipy.algorithms.clustering.utils.voronoi(x, centers)

Assignment of data items to nearest cluster center

Parameters:

x array of shape (n,p) :

n = number of items, p = data dimension

centers, array of shape (k, p) the cluster centers :

Returns:

z vector of shape(n), the resulting assignment :