diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 585b17acd..673d3edfb 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -436,3 +436,58 @@ def transform_dbscan(
                 )
             return g
         return emb, X, y, df
+
+
+
+def get_dendrogram_edges(df: pd.DataFrame, as_graph: bool = True) -> Union[pd.DataFrame, Any]:
+    """Converts a dataframe of feature embeddings to a dendrogram graph with edges between each merge
+    This will calculate what AgglomerativeClustering does under the hood, but using the linkage matrix
+
+    Args:
+        :df: dataframe of feature embeddings
+        :as_graph: whether to return a graphistry graph or a dataframe of edges
+    Usage:
+        ::
+            g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
+            g2 = g.umap().dbscan()  # or g2 = g.featurize()
+            g3 = get_dendrogram_edges(g2.get_matrix(), as_graph=True)
+    """
+    from scipy.cluster.hierarchy import linkage
+    import graphistry
+
+    # df is the numeric dataframe from umap, or featurize
+    Z = linkage(df, 'ward')
+    # Convert to a DataFrame
+    df2 = pd.DataFrame(Z, columns=['src', 'dst', 'dist', 'size'])
+
+    # Create a new node for each merge
+    num_samples = len(df)
+    df2['src'] = df2['src'].astype(int)
+    df2['dst'] = df2['dst'].astype(int)
+
+    # The new node is the index + the number of samples
+    df2['new_node'] = df2.index + num_samples
+
+    # Convert the dataframe to have each edge as a row
+    edges_src = pd.DataFrame({
+        'node1': df2['new_node'],
+        'node2': df2['src'],
+        'dist': df2['dist'], 
+    })
+
+    edges_dst = pd.DataFrame({
+        'node1': df2['new_node'],
+        'node2': df2['dst'],
+        'dist': df2['dist']
+    })
+
+    edges = pd.concat([edges_src, edges_dst])
+
+    # Handle data type
+    edges['node1'] = edges['node1'].astype(int)
+    edges['node2'] = edges['node2'].astype(int)
+    
+    if as_graph:
+        g = graphistry.edges(edges, 'node1', 'node2')
+        return g
+    return edges
diff --git a/graphistry/tests/test_compute_cluster.py b/graphistry/tests/test_compute_cluster.py
index c93d0e279..947f10ddc 100644
--- a/graphistry/tests/test_compute_cluster.py
+++ b/graphistry/tests/test_compute_cluster.py
@@ -4,7 +4,7 @@
 import graphistry
 from graphistry.constants import DBSCAN
 from graphistry.util import ModelDict
-from graphistry.compute.cluster import lazy_dbscan_import_has_dependency
+from graphistry.compute.cluster import lazy_dbscan_import_has_dependency, get_dendrogram_edges
 
 has_dbscan, _, has_gpu_dbscan, _ = lazy_dbscan_import_has_dependency()
 
@@ -67,7 +67,26 @@ def test_transform_dbscan(self):
         g3 = g2.transform_dbscan(ndf, ndf, verbose=True)
         self._condition(g3, kind)
         
-            
+
+class TestDendrogram(unittest.TestCase):
+
+    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
+    def setUp(self) -> None:
+        g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
+        gs = []
+        for kind in ['nodes', 'edges']:
+            g2 = g.umap(kind=kind, n_topics=2, dbscan=False).dbscan(kind=kind, verbose=True)
+            gs.append(g2)
+        self.gs = gs
+
+    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
+    def testDendrogramToGraph(self):
+        for kind, g2 in zip(['nodes', 'edges'], self.gs):
+            g3 = get_dendrogram_edges(g2.get_matrix(kind=kind))
+            self.assertTrue('node1' in g3._edges, 'dendrogram graph has no `node1` column')
+            self.assertTrue('node2' in g3._edges, 'dendrogram graph has no `node1` column')
+
+
 if __name__ == '__main__':
     unittest.main()