Minor cleanup Role Detection Clusterer

src/data-hub/community-detection-microservice/app/processing/clustering/clusterer.py

 import json
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.cluster import DBSCAN, OPTICS
+from sklearn.cluster import OPTICS
 from typing import List, Dict, Any, TypeVar
 from deprecated import deprecated
 
@@ -11,12 +11,12 @@ ClusterGroup = Dict[Any, List[Dict]]
 class Clusterer:
     '''
     Clusterer for applying density-based clustering on datasets.
-    The clustering is done with DBSCAN.
+    The clustering is done with OPTICS.
 
-    :param epsilon: Epsilon used in DBSCAN 
-    :param min_points: Min_points used in DBSCAN
+    :param epsilon: Eps used in OPTICS 
+    :param min_points: MinPts used in OPTICS
     '''
-    def __init__(self, epsilon=11, min_points=2):
+    def __init__(self, epsilon=11, min_points=5):
         self.epsilon = epsilon
         self.min_points = min_points
 
@@ -59,7 +59,7 @@ class Clusterer:
         if features is None or len(features) == 0:
             return features # trash in trash out
     
-        dbsc = OPTICS() # DBSCAN(eps = self.epsilon, min_samples = self.min_points)
+        dbsc = OPTICS(min_samples=self.min_points) # DBSCAN(eps = self.epsilon, min_samples = self.min_points)
         dbsc = dbsc.fit(features)
         labels = dbsc.labels_