# scipy.cluster.vq.kmeans2

scipy.cluster.vq.kmeans2(data, k, iter=10, thresh=1e-05, minit='random', missing='warn', check_finite=True, *, seed=None)

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

  The algorithm attempts to minimize the Euclidean 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’ 1-D 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 algorithm 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’, ‘++’ 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.

      ‘++’: choose k observations accordingly to the kmeans++ method (careful seeding)

      ‘matrix’: interpret the k parameter as a k by M (or length k array for 1-D 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

    seed{None, int, `numpy.random.Generator`,

      > `numpy.random.RandomState`}, optional

      Seed for initializing the pseudo-random number generator. If seed is None (or `numpy.random`), the `numpy.random.RandomState` singleton is used. If seed is an int, a new `RandomState` instance is used, seeded with seed. If seed is already a `Generator` or `RandomState` instance then that instance is used. The default is None.

  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 ith observation is closest to.

  See also

  `kmeans`

  References

  1

    D. Arthur and S. Vassilvitskii, “k-means++: the advantages of careful seeding”, Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2007.

  Examples

      >>> from scipy.cluster.vq import kmeans2
      >>> import matplotlib.pyplot as plt

  Create z, an array with shape (100, 2) containing a mixture of samples from three multivariate normal distributions.

      >>> rng = np.random.default_rng()
      >>> a = rng.multivariate_normal([0, 6], [[2, 1], [1, 1.5]], size=45)
      >>> b = rng.multivariate_normal([2, 0], [[1, -1], [-1, 3]], size=30)
      >>> c = rng.multivariate_normal([6, 4], [[5, 0], [0, 1.2]], size=25)
      >>> z = np.concatenate((a, b, c))
      >>> rng.shuffle(z)

  Compute three clusters.

      >>> centroid, label = kmeans2(z, 3, minit='points')
      >>> centroid
      array([[ 2.22274463, -0.61666946],  # may vary
             [ 0.54069047,  5.86541444],
             [ 6.73846769,  4.01991898]])

  How many points are in each cluster?

      >>> counts = np.bincount(label)
      >>> counts
      array([29, 51, 20])  # may vary

  Plot the clusters.

      >>> w0 = z[label == 0]
      >>> w1 = z[label == 1]
      >>> w2 = z[label == 2]
      >>> plt.plot(w0[:, 0], w0[:, 1], 'o', alpha=0.5, label='cluster 0')
      >>> plt.plot(w1[:, 0], w1[:, 1], 'd', alpha=0.5, label='cluster 1')
      >>> plt.plot(w2[:, 0], w2[:, 1], 's', alpha=0.5, label='cluster 2')
      >>> plt.plot(centroid[:, 0], centroid[:, 1], 'k*', label='centroids')
      >>> plt.axis('equal')
      >>> plt.legend(shadow=True)
      >>> plt.show()

scipy.cluster.vq.kmeans Hierarchical clustering (`scipy.cluster.hierarchy`)