diff --git a/bob/learn/em/__init__.py b/bob/learn/em/__init__.py
index 6d67218a8049049a3e44c11076f6f8a61a1e09dc..6c50a0a6c1a429550f2e377665a21b5d13431ce3 100644
--- a/bob/learn/em/__init__.py
+++ b/bob/learn/em/__init__.py
@@ -1,7 +1,7 @@
 import bob.extension
 
 from .gmm import GMMMachine, GMMStats
-from .k_means import KMeansMachine
+from .kmeans import KMeansMachine
 from .linear_scoring import linear_scoring  # noqa: F401
 from .wccn import WCCN
 from .whitening import Whitening
diff --git a/bob/learn/em/gmm.py b/bob/learn/em/gmm.py
index 88efe81af0e7a5918cfc3f604adf3fc906aec83d..78ab69fe2de3f1506529c220c89c5bc57f1fa805 100644
--- a/bob/learn/em/gmm.py
+++ b/bob/learn/em/gmm.py
@@ -18,7 +18,7 @@ import numpy as np
 from h5py import File as HDF5File
 from sklearn.base import BaseEstimator
 
-from .k_means import (
+from .kmeans import (
     KMeansMachine,
     array_to_delayed_list,
     check_and_persist_dask_input,
diff --git a/bob/learn/em/k_means.py b/bob/learn/em/kmeans.py
similarity index 100%
rename from bob/learn/em/k_means.py
rename to bob/learn/em/kmeans.py
diff --git a/bob/learn/em/test/test_kmeans.py b/bob/learn/em/test/test_kmeans.py
index 1400b469e909973e3a7911c7e7439a55b7d87a47..8edc205ab9ecf2f6f15a8c9e4213f57cb219f804 100644
--- a/bob/learn/em/test/test_kmeans.py
+++ b/bob/learn/em/test/test_kmeans.py
@@ -16,7 +16,7 @@ import dask.array as da
 import numpy as np
 import scipy.spatial.distance
 
-from bob.learn.em import KMeansMachine, k_means
+from bob.learn.em import KMeansMachine, kmeans
 
 
 def to_numpy(*args):
@@ -187,6 +187,6 @@ def test_get_centroids_distance():
     oracle = scipy.spatial.distance.cdist(means, data, metric="sqeuclidean")
     for transform in (to_numpy,):
         data, means = transform(data, means)
-        dist = k_means.get_centroids_distance(data, means)
+        dist = kmeans.get_centroids_distance(data, means)
         np.testing.assert_allclose(dist, oracle)
         assert type(data) is type(dist), (type(data), type(dist))