diff --git a/src/processing.py b/src/processing.py
index b39d140..c9f44b5 100644
--- a/src/processing.py
+++ b/src/processing.py
@@ -29,20 +29,22 @@ def get_furthest_element(element, data):
     furthest_index = element_df["distance"].astype(float).idxmax()
     furthest_row = data.iloc[furthest_index]
     furthest_point = get_different_element(original=element, row=furthest_row)
-    furthest_element = {"point": furthest_point, "distance": furthest_row["distance"]}
-    return furthest_element, furthest_index
+    return {"point": furthest_point, "distance": furthest_row["distance"]}
+
+
+def remove_solution_dataset(data, solution):
+    return data.query(f"source != {solution} and destination != {solution}")
 
 
-# FIXME Remove duplicated elements properly
 def greedy_algorithm(n, m, data):
     solutions = DataFrame(columns=["point", "distance"])
     first_solution = get_first_solution(n, data)
     solutions = solutions.append(first_solution, ignore_index=True)
     for _ in range(m):
-        last_solution = solutions["point"].tail(n=1)
-        centroid, index = get_furthest_element(element=int(last_solution), data=data)
+        last_solution = int(solutions["point"].tail(n=1))
+        centroid = get_furthest_element(element=last_solution, data=data)
         solutions = solutions.append(dict(centroid), ignore_index=True)
-        data = data.drop(index)
+        data = remove_solution_dataset(data=data, solution=last_solution)
     return solutions
 
 
@@ -72,9 +74,11 @@ def execute_algorithm(choice, n, m, data):
 
 def show_results(solutions):
     distance_sum = solutions["distance"].sum()
+    duplicates = solutions.duplicated()
     print(solutions)
     print("Total distance: " + str(distance_sum))
-    print(solutions.duplicated())
+    if solutions[duplicates].empty:
+        print("No duplicates found")
 
 
 def usage(argv):