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# scipy.cluster.vq.kmeans2

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

  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 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 (not used yet) minit : string 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.
  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.

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