Are there advantages of using sklearn KMeans versus SciPy kmeans?

Question

From the documentation of sklearn KMeans

class sklearn.cluster.KMeans(n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=1)

and SciPy kmeans

scipy.cluster.vq.kmeans(obs, k_or_guess, iter=20, thresh=1e-05, check_finite=True)

it is clear the number of parameters differ and perhaps more of them are available for sklearn.

Have any of you tried one versus the other and would you have a preference for using one of them in a classification problem?

Answer 1

Benchmark .

And you will never touch the scipy one again.