diff --git a/AIMSim/ops/clustering.py b/AIMSim/ops/clustering.py
index 21221038..5b47f5ed 100644
--- a/AIMSim/ops/clustering.py
+++ b/AIMSim/ops/clustering.py
@@ -1,10 +1,80 @@
+"""Operation for clustering molecules"""
 import sklearn.exceptions
 from sklearn.cluster import AgglomerativeClustering
 from sklearn_extra.cluster import KMedoids as SklearnExtraKMedoids
 
 
 class Cluster:
+    """
+    Wrapper class for different clustering algorithms.
+    Attributes:
+        clustering_method (str): Label for the specific algorithm used.
+            Supported methods are:
+            'kmedoids' for the K-Medoids algorithm [1]. This method is useful
+                when the molecular descriptors are continuous / Euclidean
+                since it relies on the existence of a sensible medoid.
+            'complete_linkage', 'complete' for complete linkage agglomerative
+                hierarchical clustering [2].
+            'average_linkage', 'average' for average linkage agglomerative
+                hierarchical clustering [2].
+            'single_linkage', 'single' for single linkage agglomerative
+                hierarchical clustering [2].
+            'ward' for Ward's algorithm [2]. This method is useful for
+                Euclidean descriptors.
+        n_clusters (int): Number of clusters.
+        model_ (sklearn.cluster.AgglomerativeClustering or
+                sklearn_extra.cluster.KMedoids): The clustering estimator.
+        labels_ (np.ndarray of shape (n_samples,)):
+            cluster labels of the training set samples.
+
+    Methods:
+        fit(X): Fit the estimator.
+        predict(X): Get prediction from the estimator.
+        get_labels: Get cluster labels of the training set samples.
+
+    References:
+        [1] Hastie, T., Tibshirani R. and Friedman J.,
+            The Elements of statistical Learning: Data Mining, Inference,
+            and Prediction, 2nd Ed., Springer Series in Statistics (2009).
+        [2] Murtagh, F. and Contreras, P., Algorithms for hierarchical
+            clustering: an overview. WIREs Data Mining Knowl Discov
+            (2011). https://doi.org/10.1002/widm.53
+
+    """
     def __init__(self, n_clusters, clustering_method, **kwargs):
+        """
+        Constructor for the Cluster class.
+        Args:
+            n_clusters (int): Number of clusters.
+            clustering_method(str): Label for the specific algorithm used.
+                Supported methods are:
+                'kmedoids' for the K-Medoids algorithm [1]. This method is
+                    useful when the molecular descriptors are continuous
+                    / Euclidean since it relies on the existence of a
+                    sensible medoid.
+                'complete_linkage', 'complete' for complete linkage
+                    agglomerative hierarchical clustering [2].
+                'average_linkage', 'average' for average linkage agglomerative
+                    hierarchical clustering [2].
+                'single_linkage', 'single' for single linkage agglomerative
+                    hierarchical clustering [2].
+                'ward' for Ward's algorithm [2]. This method is useful for
+                    Euclidean descriptors.
+            kwargs (dict): Keyword arguments. These are passed to the
+                estimators. Refer to the following documentation page for
+                kmedoids:
+                [https://scikit-learn-extra.readthedocs.io/en/stable/generated/sklearn_extra.cluster.KMedoids.html]
+                agglomerative hierarchical clustering:
+                [https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html]
+
+        References:
+        [1] Hastie, T., Tibshirani R. and Friedman J.,
+            The Elements of statistical Learning: Data Mining, Inference,
+            and Prediction, 2nd Ed., Springer Series in Statistics (2009).
+        [2] Murtagh, F. and Contreras, P., Algorithms for hierarchical
+            clustering: an overview. WIREs Data Mining Knowl Discov
+            (2011). https://doi.org/10.1002/widm.53
+        """
         self.clustering_method = clustering_method
         self.n_clusters = n_clusters
         if self.clustering_method == "kmedoids":
@@ -25,12 +95,25 @@ def __init__(self, n_clusters, clustering_method, **kwargs):
             raise ValueError(f"{clustering_method} not implemented")
 
     def _get_kmedoids_model_(self, **kwargs):
-        max_iter = kwargs.get("max_iter", 300)
+        """
+        Initialize a k-medoids model.
+
+        Args:
+        kwargs (dict): Keyword arguments. These are passed to the
+                estimators. Refer to the following documentation page for
+                kmedoids:
+                [https://scikit-learn-extra.readthedocs.io/en/stable/generated/sklearn_extra.cluster.KMedoids.html]
+
+        """
+        _ = kwargs.pop('metric', None)
         return SklearnExtraKMedoids(
-            n_clusters=self.n_clusters, metric="precomputed", max_iter=max_iter
+            n_clusters=self.n_clusters,
+            metric="precomputed",
+            **kwargs
         )
 
     def _get_linkage_model(self, linkage_method, **kwargs):
+        _ = kwargs.pop('affinity', None)
         return AgglomerativeClustering(
             n_clusters=self.n_clusters,
             affinity="precomputed",
@@ -39,7 +122,7 @@ def _get_linkage_model(self, linkage_method, **kwargs):
         )
 
     def fit(self, X):
-        """Call the model fit method.
+        """Fit the estimator.
         Args:
             X (np.ndarray or list):  Distance matrix.
         """
@@ -48,18 +131,39 @@ def fit(self, X):
         return self
 
     def predict(self, X):
+        """Get predictions from the estimator.
+        Args:
+            X (np.ndarray or list):  samples to predict on.
+        Raises:
+            sklearn.exceptions.NotFittedError if estimator is not fitted.
+        """
         try:
             return self.model_.predict(X)
         except sklearn.exceptions.NotFittedError as e:
             raise e
 
     def get_labels(self):
+        """
+        Get cluster labels of the training set samples.
+        Returns:
+            np.ndarray of shape (n_samples,)): Returns self.labels_,
+                cluster labels of the training set samples.
+        """
         if not self._is_fitted():
             raise sklearn.exceptions.NotFittedError
         return self.labels_
 
     def __str__(self):
+        """
+        String representation of the class.
+        Returns:
+            (str): Returns the self.clustering_method attribute.
+        """
         return self.clustering_method
 
     def _is_fitted(self):
+        """
+        Returns:
+            (bool): True is self.labels_ exists.
+        """
         return hasattr(self, "labels_")