def minmax(data):
    data = StandardScaler().fit_transform(data)
    centers = data[0:1]
    centers_ids = set([0])
    offset = 0
    while True:
        distances = np.zeros((data.shape[0], centers.shape[0]))
        for i in range(centers.shape[0]):
            for j in range(data.shape[0]):
                if j not in centers_ids:
                    distances[j, i] = dist(data[j], centers[i])
                else:
                    distances[j, i] = 0
        min_distances = np.min(distances, axis = 1)
        L = np.argmax(min_distances)
        if min_distances[L] > (offset / centers.shape[0]):
            centers = np.append(centers, data[L:L+1], axis = 0)
            offset += min_distances[L]
            centers_ids.add(L)
        else:
            min_distances = np.argmin(distances, axis = 1)
            return (centers, min_distances)