Module « scipy.cluster.hierarchy »

Fonction fclusterdata - module scipy.cluster.hierarchy

Signature de la fonction fclusterdata

def fclusterdata(X, t, criterion='inconsistent', metric='euclidean', depth=2, method='single', R=None) 

Description

fclusterdata.__doc__

    Cluster observation data using a given metric.

    Clusters the original observations in the n-by-m data
    matrix X (n observations in m dimensions), using the euclidean
    distance metric to calculate distances between original observations,
    performs hierarchical clustering using the single linkage algorithm,
    and forms flat clusters using the inconsistency method with `t` as the
    cut-off threshold.

    A 1-D array ``T`` of length ``n`` is returned. ``T[i]`` is
    the index of the flat cluster to which the original observation ``i``
    belongs.

    Parameters
    ----------
    X : (N, M) ndarray
        N by M data matrix with N observations in M dimensions.
    t : scalar
        For criteria 'inconsistent', 'distance' or 'monocrit',
         this is the threshold to apply when forming flat clusters.
        For 'maxclust' or 'maxclust_monocrit' criteria,
         this would be max number of clusters requested.
    criterion : str, optional
        Specifies the criterion for forming flat clusters. Valid
        values are 'inconsistent' (default), 'distance', or 'maxclust'
        cluster formation algorithms. See `fcluster` for descriptions.
    metric : str or function, optional
        The distance metric for calculating pairwise distances. See
        ``distance.pdist`` for descriptions and linkage to verify
        compatibility with the linkage method.
    depth : int, optional
        The maximum depth for the inconsistency calculation. See
        `inconsistent` for more information.
    method : str, optional
        The linkage method to use (single, complete, average,
        weighted, median centroid, ward). See `linkage` for more
        information. Default is "single".
    R : ndarray, optional
        The inconsistency matrix. It will be computed if necessary
        if it is not passed.

    Returns
    -------
    fclusterdata : ndarray
        A vector of length n. T[i] is the flat cluster number to
        which original observation i belongs.

    See Also
    --------
    scipy.spatial.distance.pdist : pairwise distance metrics

    Notes
    -----
    This function is similar to the MATLAB function ``clusterdata``.

    Examples
    --------
    >>> from scipy.cluster.hierarchy import fclusterdata

    This is a convenience method that abstracts all the steps to perform in a
    typical SciPy's hierarchical clustering workflow.

    * Transform the input data into a condensed matrix with `scipy.spatial.distance.pdist`.

    * Apply a clustering method.

    * Obtain flat clusters at a user defined distance threshold ``t`` using `scipy.cluster.hierarchy.fcluster`.

    >>> X = [[0, 0], [0, 1], [1, 0],
    ...      [0, 4], [0, 3], [1, 4],
    ...      [4, 0], [3, 0], [4, 1],
    ...      [4, 4], [3, 4], [4, 3]]

    >>> fclusterdata(X, t=1)
    array([3, 3, 3, 4, 4, 4, 2, 2, 2, 1, 1, 1], dtype=int32)

    The output here (for the dataset ``X``, distance threshold ``t``, and the
    default settings) is four clusters with three data points each.