diff --git a/hc/tests/__init__.py b/hc/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/hc/tests/test_autoClustering.py b/hc/tests/test_autoClustering.py
new file mode 100644
index 0000000..1f0e9b8
--- /dev/null
+++ b/hc/tests/test_autoClustering.py
@@ -0,0 +1,60 @@
+import pytest
+import numpy as np
+from hypergraph_clustering.clustering.auto_clustering import AutoClusterHypergraphClustering
+
+
+@pytest.mark.parametrize("adjacency_matrix, max_clusters", [
+    (np.array([
+        [0, 1, 0, 0],
+        [1, 0, 1, 1],
+        [0, 1, 0, 1],
+        [0, 1, 1, 0],
+    ]), 3),
+    (np.array([
+        [0, 1, 1],
+        [1, 0, 1],
+        [1, 1, 0],
+    ]), 2),
+])
+def test_silhouette_score_calculation(adjacency_matrix, max_clusters):
+    clustering = AutoClusterHypergraphClustering(linkage="average", max_clusters=max_clusters, scoring="silhouette")
+    labels = clustering.fit(adjacency_matrix)
+    assert len(labels) == adjacency_matrix.shape[0]
+    assert clustering.best_n_clusters is not None
+    assert clustering.best_score is not None
+
+
+@pytest.mark.parametrize("adjacency_matrix", [
+    np.array([
+        [0, 1, 0, 0],
+        [1, 0, 1, 1],
+        [0, 1, 0, 1],
+        [0, 1, 1, 0],
+    ]),
+])
+def test_best_score_non_negative(adjacency_matrix):
+    clustering = AutoClusterHypergraphClustering(linkage="average", max_clusters=3, scoring="silhouette")
+    clustering.fit(adjacency_matrix)
+    assert clustering.best_score >= 0
+
+def test_invalid_scoring_metric():
+    adjacency_matrix = np.array([
+        [0, 1, 0],
+        [1, 0, 1],
+        [0, 1, 0],
+    ])
+    clustering = AutoClusterHypergraphClustering(linkage="average", max_clusters=5, scoring="invalid_metric")
+    with pytest.raises(ValueError, match=".*Неизвестная метрика оценки.*"):
+        clustering.fit(adjacency_matrix)
+
+@pytest.mark.parametrize("adjacency_matrix, scoring, expected_exception", [
+    (np.array([
+        [0, 1, 0],
+        [1, 0, 1],
+        [0, 1, 0],
+    ]), "unsupported_metric", ValueError),
+])
+def test_invalid_scoring(adjacency_matrix, scoring, expected_exception):
+    clustering = AutoClusterHypergraphClustering(linkage="average", max_clusters=3, scoring=scoring)
+    with pytest.raises(expected_exception):
+        clustering.fit(adjacency_matrix)
\ No newline at end of file
diff --git a/hc/tests/test_clustering.py b/hc/tests/test_clustering.py
new file mode 100644
index 0000000..20a74da
--- /dev/null
+++ b/hc/tests/test_clustering.py
@@ -0,0 +1,36 @@
+import pytest
+import numpy as np
+from hypergraph_clustering.utils.graph_conversion import hypergraph_to_incidence_matrix, incidence_to_adjacency
+from hypergraph_clustering.clustering.agglomerative import AgglomerativeHypergraphClustering
+
+
+@pytest.mark.parametrize("hyperedges, expected_shape", [
+    ([[0, 1, 2], [1, 2, 3], [3, 4]], (5, 3)),
+    ([[0, 1], [1, 2], [2, 3], [3, 4], [4, 0]], (5, 5)),
+])
+def test_incidence_matrix_shape(hyperedges, expected_shape):
+    incidence_matrix = hypergraph_to_incidence_matrix(hyperedges)
+    assert incidence_matrix.shape == expected_shape
+
+
+@pytest.mark.parametrize("hyperedges", [
+    [[0, 1, 2], [1, 2, 3], [3, 4]],
+    [[0, 1], [2, 3], [4, 5]],
+])
+def test_adjacency_matrix_symmetry(hyperedges):
+    incidence_matrix = hypergraph_to_incidence_matrix(hyperedges)
+    adjacency_matrix = incidence_to_adjacency(incidence_matrix)
+    assert np.allclose(adjacency_matrix, adjacency_matrix.T)
+
+
+@pytest.mark.parametrize("hyperedges, n_clusters, expected_cluster_range", [
+    ([[0, 1, 2], [1, 2, 3], [3, 4]], 2, {0, 1}),
+    ([[0, 1], [2, 3], [4, 5]], 3, {0, 1, 2}),
+])
+def test_agglomerative_clustering_labels(hyperedges, n_clusters, expected_cluster_range):
+    incidence_matrix = hypergraph_to_incidence_matrix(hyperedges)
+    adjacency_matrix = incidence_to_adjacency(incidence_matrix)
+    clustering = AgglomerativeHypergraphClustering(n_clusters=n_clusters)
+    labels = clustering.fit(adjacency_matrix)
+    assert len(labels) == adjacency_matrix.shape[0]
+    assert set(labels).issubset(expected_cluster_range)