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# scipy.cluster.vq.kmeans2

`scipy.cluster.vq.``kmeans2`(data, k, iter=10, thresh=1e-05, minit='random', missing='warn', check_finite=True)

  Classify a set of observations into k clusters using the k-means algorithm.

  The algorithm attempts to minimize the Euclidian distance between observations and centroids. Several initialization methods are included.

  Parameters:

  data : ndarray

  > A ‘M’ by ‘N’ array of ‘M’ observations in ‘N’ dimensions or a length ‘M’ array of ‘M’ one-dimensional observations.

  k : int or ndarray

  > The number of clusters to form as well as the number of centroids to generate. If minit initialization string is ‘matrix’, or if a ndarray is given instead, it is interpreted as initial cluster to use instead.

  iter : int, optional

  > Number of iterations of the k-means algrithm to run. Note that this differs in meaning from the iters parameter to the kmeans function.

  thresh : float, optional

  > (not used yet)

  minit : str, optional

  > Method for initialization. Available methods are ‘random’, ‘points’, ‘uniform’, and ‘matrix’:

  > ‘random’: generate k centroids from a Gaussian with mean and variance estimated from the data.

  > ‘points’: choose k observations (rows) at random from data for the initial centroids.

  > ‘uniform’: generate k observations from the data from a uniform distribution defined by the data set (unsupported).

  > ‘matrix’: interpret the k parameter as a k by M (or length k array for one-dimensional data) array of initial centroids.

  missing : str, optional

  > Method to deal with empty clusters. Available methods are ‘warn’ and ‘raise’:

  > ‘warn’: give a warning and continue.

  > ‘raise’: raise an ClusterError and terminate the algorithm.

  check_finite : bool, optional

  > Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. Default: True

  Returns:

  centroid : ndarray

  > A ‘k’ by ‘N’ array of centroids found at the last iteration of k-means.

  label : ndarray

  > label[i] is the code or index of the centroid the i’th observation is closest to.