diff --git a/frontend/clusters.py b/frontend/clusters.py
new file mode 100644
index 0000000..ac2af4c
--- /dev/null
+++ b/frontend/clusters.py
@@ -0,0 +1,63 @@
+from sklearn.cluster import DBSCAN, KMeans
+import numpy as np
+
+class DBSCAN_cluster():
+    def __init__(self, eps, min_samples,data):
+        self.eps = eps
+        self.min_samples = min_samples
+        self.data = data
+        self.labels = np.array([])
+
+    def run(self):
+        dbscan = DBSCAN(eps=self.eps, min_samples=self.min_samples)
+        self.labels = dbscan.fit_predict(self.data)
+        return self.labels
+
+    def get_stats(self):
+        unique_labels = np.unique(self.labels)
+        stats = []
+        for label in unique_labels:
+            if label == -1:
+                continue
+            cluster_points = self.data[self.labels == label]
+            num_points = len(cluster_points)
+            density = num_points / (np.max(cluster_points, axis=0) - np.min(cluster_points, axis=0)).prod()
+            stats.append({
+                "cluster": label,
+                "num_points": num_points,
+                "density": density
+            })
+
+        return stats
+    
+
+class KMeans_cluster():
+    def __init__(self, n_clusters, n_init, max_iter, data):
+        self.n_clusters = n_clusters
+        self.n_init = n_init
+        self.max_iter = max_iter
+        self.data = data
+        self.labels = np.array([])
+        self.centers = []
+
+    def run(self):
+        kmeans = KMeans(n_clusters=self.n_clusters, init="random", n_init=self.n_init, max_iter=self.max_iter, random_state=111)
+        self.labels = kmeans.fit_predict(self.data)
+        self.centers = kmeans.cluster_centers_
+        return self.labels
+
+
+    def get_stats(self):
+        unique_labels = np.unique(self.labels)
+        stats = []
+
+        for label in unique_labels:
+            cluster_points = self.data[self.labels == label]
+            num_points = len(cluster_points)
+            center = self.centers[label]
+            stats.append({
+                'cluster': label,
+                'num_points': num_points,
+                'center': center
+            })
+        return stats
diff --git a/frontend/pages/clustering_dbscan.py b/frontend/pages/clustering_dbscan.py
index d06b10a..7ca16f6 100644
--- a/frontend/pages/clustering_dbscan.py
+++ b/frontend/pages/clustering_dbscan.py
@@ -1,10 +1,9 @@
 import streamlit as st
 import matplotlib.pyplot as plt
-from sklearn.cluster import DBSCAN
+from clusters import DBSCAN_cluster
 
 st.header("Clustering: dbscan")
 
-
 if "data" in st.session_state:
     data = st.session_state.data
 
@@ -17,8 +16,9 @@ if "data" in st.session_state:
     if len(data_name) >= 2 and len(data_name) <=3:
         x = data[data_name].to_numpy()
 
-        dbscan = DBSCAN(eps=eps, min_samples=min_samples)
-        y_dbscan = dbscan.fit_predict(x)
+        dbscan = DBSCAN_cluster(eps,min_samples,x)
+        y_dbscan = dbscan.run()
+        st.table(dbscan.get_stats())
 
         fig = plt.figure()
         if len(data_name) == 2:
@@ -28,8 +28,5 @@ if "data" in st.session_state:
             ax = fig.add_subplot(projection='3d')
             ax.scatter(x[:, 0], x[:, 1],x[:, 2], c=y_dbscan, s=50, cmap="viridis")
         st.pyplot(fig)
-
-    
-
 else:
     st.error("file not loaded")
\ No newline at end of file
diff --git a/frontend/pages/clustering_kmeans.py b/frontend/pages/clustering_kmeans.py
index c61bf40..63c7d55 100644
--- a/frontend/pages/clustering_kmeans.py
+++ b/frontend/pages/clustering_kmeans.py
@@ -1,10 +1,9 @@
 import streamlit as st
-from sklearn.cluster import KMeans
 import matplotlib.pyplot as plt
+from clusters import KMeans_cluster
 
 st.header("Clustering: kmeans")
 
-
 if "data" in st.session_state:
     data = st.session_state.data
 
@@ -23,21 +22,22 @@ if "data" in st.session_state:
     if len(data_name) >= 2 and len(data_name) <=3:
         x = data[data_name].to_numpy()
 
-        kmeans = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, max_iter=max_iter, random_state=111)
-        y_kmeans = kmeans.fit_predict(x)
+        kmeans = KMeans_cluster(n_clusters, n_init, max_iter, x)
+        y_kmeans = kmeans.run()
+
+        st.table(kmeans.get_stats())
 
+        centers = kmeans.centers
         fig = plt.figure()
         if len(data_name) == 2:
             ax = fig.add_subplot(projection='rectilinear')
             plt.scatter(x[:, 0], x[:, 1], c=y_kmeans, s=50, cmap="viridis")
-            centers = kmeans.cluster_centers_
             plt.scatter(centers[:, 0], centers[:, 1], c="black", s=200, marker="X")
         else:
             ax = fig.add_subplot(projection='3d')
 
             ax.scatter(x[:, 0], x[:, 1],x[:, 2], c=y_kmeans, s=50, cmap="viridis")
-            centers = kmeans.cluster_centers_
-            ax.scatter(centers[:, 0], centers[:, 1],centers[:, 2], c="black", s=200, marker="X")
+            ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c="black", s=200, marker="X")
         st.pyplot(fig)
 
 else: