diff --git a/experiments/benchmark.py b/experiments/benchmark.py
index 3e51b27..dd704a9 100644
--- a/experiments/benchmark.py
+++ b/experiments/benchmark.py
@@ -14,45 +14,45 @@ with open('./data/universities.csv', 'r') as f:
 
     print('Universities: %i' % len(universities))
 
-    start = timer()
-    clusters = list(pairwise_leader(universities, distance=levenshtein, radius=2))
-    print('Leader (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(pairwise_leader(universities, distance=levenshtein, radius=2))
+    # print('Leader (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(pairwise_fuzzy_clusters(universities, distance=levenshtein, radius=2))
-    print('Fuzzy clusters (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(pairwise_fuzzy_clusters(universities, distance=levenshtein, radius=2))
+    # print('Fuzzy clusters (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(pairwise_connected_components(universities, distance=levenshtein, radius=2))
-    print('Connected components (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(pairwise_connected_components(universities, distance=levenshtein, radius=2))
+    # print('Connected components (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(vp_tree(universities, distance=levenshtein, radius=2))
-    print('VPTree (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(vp_tree(universities, distance=levenshtein, radius=2))
+    # print('VPTree (%i):' % len(clusters), timer() - start)
 
     start = timer()
     clusters = list(quickjoin(universities, distance=levenshtein, radius=2))
     print('QuickJoin (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(nn_descent(universities, distance=levenshtein, radius=2))
-    print('NN-Descent (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(nn_descent(universities, distance=levenshtein, radius=2))
+    # print('NN-Descent (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(blocking(universities, blocks=partial(ngrams, 6), distance=levenshtein, radius=2))
-    print('Blocking (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(blocking(universities, blocks=partial(ngrams, 6), distance=levenshtein, radius=2))
+    # print('Blocking (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(sorted_neighborhood(universities, key=omission_key, distance=levenshtein, radius=2))
-    print('SNM Omission (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(sorted_neighborhood(universities, key=omission_key, distance=levenshtein, radius=2))
+    # print('SNM Omission (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(sorted_neighborhood(universities, key=skeleton_key, distance=levenshtein, radius=2))
-    print('SNM Skeleton (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(sorted_neighborhood(universities, key=skeleton_key, distance=levenshtein, radius=2))
+    # print('SNM Skeleton (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(sorted_neighborhood(universities, keys=[omission_key, skeleton_key], distance=levenshtein, radius=2))
-    print('SNM Omission + Skeleton (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(sorted_neighborhood(universities, keys=[omission_key, skeleton_key], distance=levenshtein, radius=2))
+    # print('SNM Omission + Skeleton (%i):' % len(clusters), timer() - start)
 
 print()
 with open('./data/musicians.csv', 'r') as f:
@@ -62,54 +62,63 @@ with open('./data/musicians.csv', 'r') as f:
 
     print('Artists: %i' % len(artists))
 
-    start = timer()
-    clusters = list(key_collision(artists, keys=lambda x: ngrams(12, x), merge=True))
-    print('12-grams key collision (%i)' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(key_collision(artists, keys=lambda x: ngrams(12, x), merge=True))
+    # print('12-grams key collision (%i)' % len(clusters), timer() - start)
+
+    # start = timer()
+    # clusters = list(key_collision(artists, key=fingerprint))
+    # print('Fingerprint key collision (%i)' % len(clusters), timer() - start)
+
+    # start = timer()
+    # clusters = list(blocking(artists, blocks=partial(ngrams, 6), distance=levenshtein, radius=2, processes=8))
+    # print('Blocking (%i):' % len(clusters), timer() - start)
+
+    # start = timer()
+    # clusters = list(sorted_neighborhood(artists, key=omission_key, distance=levenshtein, radius=2))
+    # print('SNM Omission (%i):' % len(clusters), timer() - start)
+
+    # start = timer()
+    # clusters = list(sorted_neighborhood(artists, key=skeleton_key, distance=levenshtein, radius=2))
+    # print('SNM Skeleton (%i):' % len(clusters), timer() - start)
+
+    # start = timer()
+    # clusters = list(sorted_neighborhood(artists, keys=[omission_key, skeleton_key], distance=levenshtein, radius=2))
+    # print('SNM Omission + Skeleton (%i):' % len(clusters), timer() - start)
+
+    c = 0
+    g = len(artists) * (len(artists) - 1) / 2
+    def counting_levenshtein(a, b):
+        global c
+        c += 1
+
+        return levenshtein(a, b)
 
     start = timer()
-    clusters = list(key_collision(artists, key=fingerprint))
-    print('Fingerprint key collision (%i)' % len(clusters), timer() - start)
-
-    start = timer()
-    clusters = list(blocking(artists, blocks=partial(ngrams, 6), distance=levenshtein, radius=2, processes=8))
-    print('Blocking (%i):' % len(clusters), timer() - start)
-
-    start = timer()
-    clusters = list(sorted_neighborhood(artists, key=omission_key, distance=levenshtein, radius=2))
-    print('SNM Omission (%i):' % len(clusters), timer() - start)
-
-    start = timer()
-    clusters = list(sorted_neighborhood(artists, key=skeleton_key, distance=levenshtein, radius=2))
-    print('SNM Skeleton (%i):' % len(clusters), timer() - start)
-
-    start = timer()
-    clusters = list(sorted_neighborhood(artists, keys=[omission_key, skeleton_key], distance=levenshtein, radius=2))
-    print('SNM Omission + Skeleton (%i):' % len(clusters), timer() - start)
-
-    start = timer()
-    clusters = list(quickjoin(artists, distance=levenshtein, radius=2, processes=8))
+    clusters = list(quickjoin(artists, distance=counting_levenshtein, radius=2, processes=8))
     print('QuickJoin (%i):' % len(clusters), timer() - start)
+    print('c', c, 'vs.', g, 'ratio', c / g)
 
-    start = timer()
-    clusters = list(nn_descent(artists, distance=levenshtein, radius=2))
-    print('NN-Descent (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(nn_descent(artists, distance=levenshtein, radius=2))
+    # print('NN-Descent (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(minhash(artists, radius=0.8, key=lambda x: list(ngrams(5, x)), use_numpy=True))
-    print('MinHash (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(minhash(artists, radius=0.8, key=lambda x: list(ngrams(5, x)), use_numpy=True))
+    # print('MinHash (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(jaccard_intersection_index(artists, radius=0.8, key=lambda x: list(ngrams(5, x))))
-    print('Jaccard Intersection Index (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(jaccard_intersection_index(artists, radius=0.8, key=lambda x: list(ngrams(5, x))))
+    # print('Jaccard Intersection Index (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(pairwise_fuzzy_clusters(artists, distance=levenshtein, radius=2, processes=8))
-    print('Parallel Fuzzy clusters (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(pairwise_fuzzy_clusters(artists, distance=levenshtein, radius=2, processes=8))
+    # print('Parallel Fuzzy clusters (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(pairwise_connected_components(artists, distance=levenshtein, radius=2, processes=8))
-    print('Parallel connected components (%i):' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(pairwise_connected_components(artists, distance=levenshtein, radius=2, processes=8))
+    # print('Parallel connected components (%i):' % len(clusters), timer() - start)
 
-    start = timer()
-    clusters = list(vp_tree(artists, distance=levenshtein, radius=2))
-    print('VPTree clusters (%i)' % len(clusters), timer() - start)
+    # start = timer()
+    # clusters = list(vp_tree(artists, distance=levenshtein, radius=2))
+    # print('VPTree clusters (%i)' % len(clusters), timer() - start)
diff --git a/fog/clustering/quickjoin.py b/fog/clustering/quickjoin.py
index e20cc24..c16c086 100644
--- a/fog/clustering/quickjoin.py
+++ b/fog/clustering/quickjoin.py
@@ -150,7 +150,7 @@ def quickjoin_join_pivots(S1, S2, distance, radius):
             f = False
 
             for l in range(k):
-                if P[l * N2 + j] - D[l] > radius:
+                if abs(P[l * N2 + j] - D[l]) > radius:
                     f = True
                     break