diff --git a/frontend/pages/clustering:_dbscan.py b/frontend/pages/clustering:_dbscan.py
new file mode 100644
index 0000000..02fde08
--- /dev/null
+++ b/frontend/pages/clustering:_dbscan.py
@@ -0,0 +1,18 @@
+import streamlit as st
+import matplotlib.pyplot as plt
+from sklearn.cluster import DBSCAN
+import numpy as np
+
+st.header("Clustering: dbscan")
+
+
+if "data" in st.session_state:
+    data = st.session_state.data
+
+    with st.form("my_form"):
+        data_name = st.multiselect("Data Name",data.select_dtypes(include="number").columns, max_selections=2)
+        st.form_submit_button('launch')
+
+
+else:
+    st.error("file not loaded")
\ No newline at end of file
diff --git a/frontend/pages/clustering.py b/frontend/pages/clustering:_kmeans.py
similarity index 80%
rename from frontend/pages/clustering.py
rename to frontend/pages/clustering:_kmeans.py
index 97698ec..ce34e66 100644
--- a/frontend/pages/clustering.py
+++ b/frontend/pages/clustering:_kmeans.py
@@ -2,7 +2,7 @@ import streamlit as st
 from sklearn.cluster import KMeans
 import matplotlib.pyplot as plt
 
-st.header("Clustering")
+st.header("Clustering: kmeans")
 
 
 if "data" in st.session_state:
@@ -27,8 +27,9 @@ if "data" in st.session_state:
         y_kmeans = kmeans.fit_predict(x)
 
         fig, ax = plt.subplots(figsize=(12,8))
-        plt.scatter(x[:, 0], x[:, 1], s=100, c=kmeans.labels_, cmap='Set1')
-        plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=400, marker='*', color='k')
+        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')
         st.pyplot(fig)
 
 else: