diff --git a/aeon/clustering/feature_based/tests/__init__.py b/aeon/clustering/feature_based/tests/__init__.py
new file mode 100644
index 0000000000..3baec9f87b
--- /dev/null
+++ b/aeon/clustering/feature_based/tests/__init__.py
@@ -0,0 +1 @@
+"""Feature Based learning clustering tests."""
\ No newline at end of file
diff --git a/aeon/clustering/feature_based/tests/test_catch22.py b/aeon/clustering/feature_based/tests/test_catch22.py
new file mode 100644
index 0000000000..6c6a5d1cdb
--- /dev/null
+++ b/aeon/clustering/feature_based/tests/test_catch22.py
@@ -0,0 +1,73 @@
+import numpy as np
+from sklearn import metrics
+
+from aeon.clustering.feature_based import Catch22Clusterer
+from aeon.datasets import load_basic_motions, load_gunpoint
+
+def test_catch22_multivariate():
+    """Test Catch22 Clusterer with univariate data."""
+    X_train, y_train = load_basic_motions(split="train")
+    X_test, y_test = load_basic_motions(split="test")
+    num_points = 20
+
+    X_train = X_train[:num_points]
+    y_train = y_train[:num_points]
+    X_test = X_test[:num_points]
+    y_test = y_test[:num_points]
+
+    catach22 = Catch22Clusterer(
+        random_state=1,
+
+    )
+    train_result = catach22.fit_predict(X_train)
+    train_score = metrics.rand_score(y_train, train_result)
+    test_result = catach22.predict(X_test)
+    test_score = metrics.rand_score(y_test, test_result)
+
+    assert np.array_equal(
+        test_result,
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 6, 3, 3, 6, 5, 3, 1, 1, 3],
+    )
+    assert np.array_equal(
+        train_result,
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 7, 2, 4, 3, 3, 6, 1],
+    )
+    assert train_score == 0.6684210526315789
+    assert test_score == 0.8263157894736842
+    assert test_result.shape == (20,)
+    assert train_result.shape == (20,)
+
+
+def test_catch22_univariate():
+    """Test Catch22 Clusterer with multivariate data."""
+    X_train, y_train = load_gunpoint(split="train")
+    X_test, y_test = load_gunpoint(split="test")
+    num_points = 20
+
+    X_train = X_train[:num_points]
+    y_train = y_train[:num_points]
+    X_test = X_test[:num_points]
+    y_test = y_test[:num_points]
+
+    catach22 = Catch22Clusterer(
+        random_state=1,
+    )
+    train_result = catach22.fit_predict(X_train)
+    train_score = metrics.rand_score(y_train, train_result)
+    test_result = catach22.predict(X_test)
+    test_score = metrics.rand_score(y_test, test_result)
+
+    assert np.array_equal(
+        test_result,
+        [1, 3, 4, 6, 7, 3, 5, 5, 6, 3, 3, 1, 3, 1, 1, 7, 3, 0, 6, 3],
+    )
+    assert np.array_equal(
+        train_result,
+        [3, 3, 7, 7, 0, 3, 2, 4, 1, 1, 6, 1, 5, 1, 3, 1, 6, 3, 1, 5],
+    )
+    assert train_score == 0.5947368421052631
+    assert test_score == 0.531578947368421
+    assert test_result.shape == (20,)
+    assert train_result.shape == (20,)
+
+
diff --git a/aeon/clustering/feature_based/tests/test_summary.py b/aeon/clustering/feature_based/tests/test_summary.py
new file mode 100644
index 0000000000..b69928b47d
--- /dev/null
+++ b/aeon/clustering/feature_based/tests/test_summary.py
@@ -0,0 +1,72 @@
+import numpy as np
+from sklearn import metrics
+
+from aeon.clustering.feature_based import SummaryClusterer
+from aeon.datasets import load_basic_motions, load_gunpoint
+
+def test_summary_univariate():
+    """Test Summary Clusterer with univariate data."""
+    X_train, y_train = load_gunpoint(split="train")
+    X_test, y_test = load_gunpoint(split="test")
+    num_points = 20
+
+    X_train = X_train[:num_points]
+    y_train = y_train[:num_points]
+    X_test = X_test[:num_points]
+    y_test = y_test[:num_points]
+
+    summary = SummaryClusterer(
+        random_state=1,
+    )
+    train_result = summary.fit_predict(X_train)
+    train_score = metrics.rand_score(y_train, train_result)
+    test_result = summary.predict(X_test)
+    test_score = metrics.rand_score(y_test, test_result)
+
+    assert np.array_equal(
+        test_result,
+        [2, 0, 4, 2, 6, 0, 1, 7, 3, 0, 6, 2, 0, 2, 2, 6, 0, 6, 2, 6],
+    )
+    assert np.array_equal(
+        train_result,
+        [6, 6, 3, 6, 0, 6, 5, 4, 1, 2, 2, 2, 1, 2, 0, 2, 3, 6, 2, 7],
+    )
+    assert train_score == 0.6052631578947368
+    assert test_score == 0.5263157894736842
+    assert test_result.shape == (20,)
+    assert train_result.shape == (20,)
+
+
+def test_summary_multivariate():
+    """Test Summary Clusterer with multivariate data."""
+    X_train, y_train = load_basic_motions(split="train")
+    X_test, y_test = load_basic_motions(split="test")
+    num_points = 20
+
+    X_train = X_train[:num_points]
+    y_train = y_train[:num_points]
+    X_test = X_test[:num_points]
+    y_test = y_test[:num_points]
+
+    summary = SummaryClusterer(
+        random_state=1,
+    )
+    train_result = summary.fit_predict(X_train)
+    train_score = metrics.rand_score(y_train, train_result)
+    test_result = summary.predict(X_test)
+    test_score = metrics.rand_score(y_test, test_result)
+
+    assert np.array_equal(
+        test_result,
+        [2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 5, 1, 5, 3, 5, 4, 4, 1, 1, 4],
+    )
+    assert np.array_equal(
+        train_result,
+        [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 5, 5, 6, 4, 7, 4, 5, 3, 1],
+    )
+    assert train_score == 0.7421052631578947
+    assert test_score == 0.7263157894736842
+    assert test_result.shape == (20,)
+    assert train_result.shape == (20,)
+
+
diff --git a/aeon/clustering/feature_based/tests/test_tsfresh.py b/aeon/clustering/feature_based/tests/test_tsfresh.py
new file mode 100644
index 0000000000..630b66f06b
--- /dev/null
+++ b/aeon/clustering/feature_based/tests/test_tsfresh.py
@@ -0,0 +1,75 @@
+import numpy as np
+from sklearn import metrics
+
+from aeon.clustering.feature_based import TSFreshClusterer
+from aeon.datasets import load_basic_motions, load_gunpoint
+
+def test_tsfresh_univariate():
+    """Test TSFresh Clusterer with univariate data."""
+    X_train, y_train = load_gunpoint(split="train")
+    X_test, y_test = load_gunpoint(split="test")
+    num_points = 20
+
+    X_train = X_train[:num_points]
+    y_train = y_train[:num_points]
+    X_test = X_test[:num_points]
+    y_test = y_test[:num_points]
+
+    tsfresh = TSFreshClusterer(
+        random_state=1,
+        n_clusters=2,
+    )
+    train_result = tsfresh.fit_predict(X_train)
+    train_score = metrics.rand_score(y_train, train_result)
+    test_result = tsfresh.predict(X_test)
+    test_score = metrics.rand_score(y_test, test_result)
+
+    assert np.array_equal(
+        train_result,
+        [3, 7, 5, 0, 0, 5, 7, 5, 0, 4, 0, 5, 6, 0, 0, 1, 6, 5, 3, 2],
+    )
+    assert np.array_equal(
+        test_result,
+        [3, 3, 0, 5, 0, 4, 6, 7, 0, 3, 5, 5, 0, 6, 0, 5, 0, 5, 0, 7],
+
+    )
+    assert train_score == 0.48947368421052634
+    assert test_score == 0.5210526315789473
+    assert test_result.shape == (20,)
+    assert train_result.shape == (20,)
+
+
+def test_tsfresh_multivariate():
+    """Test TSFresh Clusterer with multivariate data."""
+    X_train, y_train = load_basic_motions(split="train")
+    X_test, y_test = load_basic_motions(split="test")
+    num_points = 20
+
+    X_train = X_train[:num_points]
+    y_train = y_train[:num_points]
+    X_test = X_test[:num_points]
+    y_test = y_test[:num_points]
+
+    tsfresh = TSFreshClusterer(
+        random_state=1,
+        n_clusters=2,
+    )
+    train_result = tsfresh.fit_predict(X_train)
+    train_score = metrics.rand_score(y_train, train_result)
+    test_result = tsfresh.predict(X_test)
+    test_score = metrics.rand_score(y_test, test_result)
+
+    assert np.array_equal(
+        train_result,
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 4, 6, 7, 2, 5, 5, 3, 1],
+    )
+    assert np.array_equal(
+        test_result,
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 3, 3, 1, 6, 5, 1, 1, 3],
+    )
+    assert train_score == 0.7842105263157895
+    assert test_score == 0.8263157894736842
+    assert test_result.shape == (20,)
+    assert train_result.shape == (20,)
+
+