diff --git a/sklearn/cluster/tests/test_bisect_k_means.py b/sklearn/cluster/tests/test_bisect_k_means.py
index 01afd4be9c8b5..f6fb785a076d4 100644
--- a/sklearn/cluster/tests/test_bisect_k_means.py
+++ b/sklearn/cluster/tests/test_bisect_k_means.py
@@ -1,10 +1,10 @@
 import numpy as np
 import pytest
-import scipy.sparse as sp
 
 from sklearn.cluster import BisectingKMeans
 from sklearn.metrics import v_measure_score
 from sklearn.utils._testing import assert_allclose, assert_array_equal
+from sklearn.utils.fixes import CSR_CONTAINERS
 
 
 @pytest.mark.parametrize("bisecting_strategy", ["biggest_inertia", "largest_cluster"])
@@ -33,7 +33,8 @@ def test_three_clusters(bisecting_strategy, init):
     assert_allclose(v_measure_score(expected_labels, bisect_means.labels_), 1.0)
 
 
-def test_sparse():
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_sparse(csr_container):
     """Test Bisecting K-Means with sparse data.
 
     Checks if labels and centers are the same between dense and sparse.
@@ -43,7 +44,7 @@ def test_sparse():
 
     X = rng.rand(20, 2)
     X[X < 0.8] = 0
-    X_csr = sp.csr_matrix(X)
+    X_csr = csr_container(X)
 
     bisect_means = BisectingKMeans(n_clusters=3, random_state=0)
 
@@ -84,16 +85,16 @@ def test_one_cluster():
     assert_allclose(bisect_means.cluster_centers_, X.mean(axis=0).reshape(1, -1))
 
 
-@pytest.mark.parametrize("is_sparse", [True, False])
-def test_fit_predict(is_sparse):
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS + [None])
+def test_fit_predict(csr_container):
     """Check if labels from fit(X) method are same as from fit(X).predict(X)."""
     rng = np.random.RandomState(0)
 
     X = rng.rand(10, 2)
 
-    if is_sparse:
+    if csr_container is not None:
         X[X < 0.8] = 0
-        X = sp.csr_matrix(X)
+        X = csr_container(X)
 
     bisect_means = BisectingKMeans(n_clusters=3, random_state=0)
     bisect_means.fit(X)
@@ -101,15 +102,15 @@ def test_fit_predict(is_sparse):
     assert_array_equal(bisect_means.labels_, bisect_means.predict(X))
 
 
-@pytest.mark.parametrize("is_sparse", [True, False])
-def test_dtype_preserved(is_sparse, global_dtype):
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS + [None])
+def test_dtype_preserved(csr_container, global_dtype):
     """Check that centers dtype is the same as input data dtype."""
     rng = np.random.RandomState(0)
     X = rng.rand(10, 2).astype(global_dtype, copy=False)
 
-    if is_sparse:
+    if csr_container is not None:
         X[X < 0.8] = 0
-        X = sp.csr_matrix(X)
+        X = csr_container(X)
 
     km = BisectingKMeans(n_clusters=3, random_state=0)
     km.fit(X)
@@ -117,15 +118,15 @@ def test_dtype_preserved(is_sparse, global_dtype):
     assert km.cluster_centers_.dtype == global_dtype
 
 
-@pytest.mark.parametrize("is_sparse", [True, False])
-def test_float32_float64_equivalence(is_sparse):
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS + [None])
+def test_float32_float64_equivalence(csr_container):
     """Check that the results are the same between float32 and float64."""
     rng = np.random.RandomState(0)
     X = rng.rand(10, 2)
 
-    if is_sparse:
+    if csr_container is not None:
         X[X < 0.8] = 0
-        X = sp.csr_matrix(X)
+        X = csr_container(X)
 
     km64 = BisectingKMeans(n_clusters=3, random_state=0).fit(X)
     km32 = BisectingKMeans(n_clusters=3, random_state=0).fit(X.astype(np.float32))
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index a37aef144f332..8b589c9afd012 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -1002,9 +1002,9 @@ def _check_large_sparse(X, accept_large_sparse=False):
     """Raise a ValueError if X has 64bit indices and accept_large_sparse=False"""
     if not accept_large_sparse:
         supported_indices = ["int32"]
-        if X.getformat() == "coo":
+        if X.format == "coo":
             index_keys = ["col", "row"]
-        elif X.getformat() in ["csr", "csc", "bsr"]:
+        elif X.format in ["csr", "csc", "bsr"]:
             index_keys = ["indices", "indptr"]
         else:
             return