def kMedoids(D, k, tmax=100):
    # determine dimensions of distance matrix D
        m, n = D.shape
        # randomly initialize an array of k medoid indices
        M = np.sort(np.random.choice(n, k)
        # create a copy of the array of medoid indices
        Mnew = np.copy(M)
        # initialize a dictionary to represent clusters
        C = {}
        for t in range(tmax):
        # determine clusters, i.e. arrays of data indices
            J = np.argmin(D[:,M], axis=1)
            for kappa in range(k):
                C[kappa] = np.where(J==kappa)[0]
            # update cluster medoids
            for kappa in range(k):
                J = np.mean(D[np.ix_(C[kappa],C[kappa])],axis=1)
                j = np.argmin(J)
                Mnew[kappa] = C[kappa][j]
                np.sort(Mnew)
                # check for convergence
                if np.array_equal(M, Mnew):
                    break
                    M = np.copy(Mnew)
                else:
                    # final update of cluster memberships
                    J = np.argmin(D[:,M], axis=1)
                    for kappa in range(k):
                        C[kappa] = np.where(J==kappa)[0]
                        # return results
                return M, C

D = pairwise_distances(arraydata,metric='euclidean')

    # split into 2 clusters
    M, C = kMedoids(D, 2)

    print('medoids:')
    for point_idx in M:
        print(arraydata[point_idx] )

    print('')
    # array for get label
    temp = []
    indeks = []
    print('clustering result:')
    for label in C:
        for point_idx in C[label]:
            print('label {0}:　{1}'.format(label, arraydata[point_idx]))
            temp.append(label)
            indeks.append(point_idx)

clustering result:
label 0:　[0.00000000e+00 0.00000000e+00 1.00000000e+00 1.00000000e+00
1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00
1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00
1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00
1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00
1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00
1.00000000e+00 0.00000000e+00 1.00000000e+00 1.00000000e+00