diff --git a/structuretoolkit/analyse/clustering.py b/structuretoolkit/analyse/clustering.py
new file mode 100644
index 000000000..f3a65e561
--- /dev/null
+++ b/structuretoolkit/analyse/clustering.py
@@ -0,0 +1,116 @@
+import numpy as np
+from scipy.cluster.hierarchy import linkage, fcluster
+from ase.io import read
+from ase.atoms import Atoms
+from ase.visualize import view
+import matplotlib.pyplot as plt
+
+
+def compute_cluster_labels(structure, num_clusters):
+    """
+    Compute hierarchical clustering labels for an ASE Atoms structure.
+
+    Use case: Identification of inherently different parts of a single structure, i.e. separate slabs, specific phases, etc.
+    Atomic distances are the sole defining metric used for clustering.
+
+    Parameters:
+        structure (Atoms): ASE Atoms object.
+        num_clusters (int): Number of clusters to form.
+
+    Returns:
+        np.ndarray: Cluster labels for each atom.
+    """
+    if isinstance(structure, Atoms):
+        # If structure is an ASE Atoms object, use it directly
+        struct = structure
+    else:
+        raise ValueError(
+            "Invalid input for structure. Please provide an ASE Atoms object."
+        )
+
+    # Calculate the distance matrix
+    distance_matrix = struct.get_all_distances(mic=True)
+
+    # Perform hierarchical clustering
+    linkage_matrix = linkage(distance_matrix, method="ward")
+
+    # Get cluster labels for the specified number of clusters
+    cluster_labels = fcluster(linkage_matrix, num_clusters, criterion="maxclust")
+
+    return cluster_labels
+
+
+def ase_view_clusters(structure, n_clusters, target_cluster_label=1):
+    """
+    Visualize a specific cluster in an ASE Atoms structure.
+
+    Parameters:
+        structure (Atoms): ASE Atoms object.
+        target_cluster_label (int): Target cluster label to visualize (NOTE: STARTS AT 1, NOT 0 (scipy))
+    """
+    cluster_labels = compute_cluster_labels(structure, n_clusters)
+    # Print or visualize the indices of the specified cluster label
+    indices_of_cluster = np.where(cluster_labels == target_cluster_label)[0]
+    # Visualize the cluster using ASE's view function
+    view(structure[indices_of_cluster])
+
+
+def plot_clusters(structure, n_clusters=1, projection=[1, 2], figsize=(30, 10)):
+    """
+    Plot clusters in a 2D scatter plot based on hierarchical clustering.
+
+    Parameters:
+        structure (Atoms): ASE Atoms object.
+        n_clusters (int): Number of clusters to form.
+        projection (list): List of two integers specifying axes for the scatter plot.
+        figsize (tuple): Figure size.
+
+    Returns:
+        None
+    """
+    fig, ax = plt.subplots(figsize=figsize)
+
+    # Compute cluster labels
+    cluster_labels = compute_cluster_labels(structure, n_clusters)
+
+    for cluster_label in np.unique(cluster_labels):
+        cluster_data = structure.positions[cluster_labels == cluster_label]
+        ax.scatter(
+            cluster_data[:, projection[0]],
+            cluster_data[:, projection[1]],
+            label=f"Cluster {cluster_label}",
+        )
+
+    ax.set_xlabel(f"Axis {projection[0]}")
+    ax.set_ylabel(f"Axis {projection[1]}")
+    ax.set_title(
+        f"2D Projection (Axis {projection[0]}-{projection[1]}) \nHierarchical Clusters "
+    )
+    ax.legend(loc=[1.05, 0.9])
+
+    # Set aspect ratio to be equal
+    ax.set_aspect("equal")
+
+    # Set axis limits to be tight
+    ax.autoscale()
+
+
+def get_structure_clusters(structure, n_clusters=2):
+    """
+    Split an ASE Atoms structure into multiple structures based on hierarchical clustering.
+
+    Parameters:
+        structure (Atoms): ASE Atoms object.
+        n_clusters (int): Number of clusters to form.
+
+    Returns:
+        list: List of ASE Atoms structures, each corresponding to a cluster.
+    """
+    # Returns a list of structures
+    cluster_labels = compute_cluster_labels(structure, n_clusters)
+    struct_list = []
+    for target_cluster_label in np.unique(cluster_labels):
+        target_cluster_label = 1
+        indices_of_cluster = np.where(cluster_labels == target_cluster_label)[0]
+        struct_list.append(structure.copy()[indices_of_cluster])
+    return struct_list
diff --git a/structuretoolkit/analyse/strain.py b/structuretoolkit/analyse/strain.py
index 3632c9dda..f9ebb6aaa 100644
--- a/structuretoolkit/analyse/strain.py
+++ b/structuretoolkit/analyse/strain.py
@@ -6,7 +6,6 @@
 
 
 class Strain:
-
     """
     Calculate local strain of each atom following the Lagrangian strain tensor:
 
diff --git a/structuretoolkit/analyse/symmetry.py b/structuretoolkit/analyse/symmetry.py
index bebeeae37..2da660fee 100644
--- a/structuretoolkit/analyse/symmetry.py
+++ b/structuretoolkit/analyse/symmetry.py
@@ -24,7 +24,6 @@
 
 
 class Symmetry(dict):
-
     """
 
     Return a class for operations related to box symmetries. Main attributes: