How to get centroids from SciPy's hierarchical agglomerative clustering?

Question

I am using SciPy\'s hierarchical agglomerative clustering methods to cluster a m x n matrix of features, but after the clustering is complete, I can\'t seem to figure out how to

Answer 1

A possible solution is a function, which returns a codebook with the centroids like kmeans in scipy.cluster.vq does. Only thing you need is the partition as vector with flat clusters part and the original observations X

def to_codebook(X, part):
    """
    Calculates centroids according to flat cluster assignment

    Parameters
    ----------
    X : array, (n, d)
        The n original observations with d features

    part : array, (n)
        Partition vector. p[n]=c is the cluster assigned to observation n

    Returns
    -------
    codebook : array, (k, d)
        Returns a k x d codebook with k centroids
    """
    codebook = []

    for i in range(part.min(), part.max()+1):
        codebook.append(X[part == i].mean(0))

    return np.vstack(codebook)