diff --git a/experiments/minhash.py b/experiments/minhash.py
index 3b72dd9..bba9b1b 100644
--- a/experiments/minhash.py
+++ b/experiments/minhash.py
@@ -55,7 +55,7 @@ with open('./data/universities.csv', 'r') as f:
 
     key = lambda x: list(ngrams(3, x))
 
-    clusters = list(minhash(universities, precision=8, radius=0.8, key=key))
+    clusters = list(minhash(universities, h=256, radius=0.8, key=key))
 
     for cluster in clusters:
         print(cluster)
diff --git a/fog/clustering/minhash.py b/fog/clustering/minhash.py
index 730465e..f49f01a 100644
--- a/fog/clustering/minhash.py
+++ b/fog/clustering/minhash.py
@@ -11,7 +11,7 @@ from collections import defaultdict
 import math
 
 from fog.clustering.utils import merge_buckets_into_clusters
-from fog.lsh.minhash import LSBMinHash
+from fog.lsh.minhash import LSBMinHash, MinHash
 
 
 # TODO: optimize probability iteratively to find number of bands
@@ -29,9 +29,7 @@ def similarity_threshold(h, bands):
     return (1.0 / bands) ** (1 / (h / bands))
 
 
-def guess_bands(precision, radius, probability):
-    h = precision * 64
-
+def guess_bands(h, radius, probability):
     bands = 1
 
     while bands <= h:
@@ -55,12 +53,40 @@ def guess_bands(precision, radius, probability):
 # TODO: faster hashing https://stackoverflow.com/questions/19701052/how-many-hash-functions-are-required-in-a-minhash-algorithm
 
 
-def minhash(data, precision=4, key=None, radius=0.8, probability=0.9):
+def minhash(data, h=256, key=None, radius=0.8, probability=0.9):
+
+    bands = guess_bands(h, radius, probability)
+    rows = h // bands
+
+    print(bands)
+    print(match_probability(h, bands, radius))
+    print(similarity_threshold(h, bands))
+
+    mh = MinHash(h)
+
+    buckets = defaultdict(list)
+
+    for item in data:
+        k = item
+
+        if key is not None:
+            k = key(item)
+
+        signature = mh.create_signature(k)
+
+        for band in range(0, h, rows):
+            band_key = (band, '%'.join(str(n) for n in signature[band:band + rows]))
+            buckets[band_key].append(item)
+
+    yield from merge_buckets_into_clusters(buckets.values(), mode='connected_components')
+
+
+def minhash_lsb(data, precision=4, key=None, radius=0.8, probability=0.9):
 
     h = precision * 64
 
     # NOTE: it seems we need to divide the bands by 2 because of LSB
-    bands = max(1, guess_bands(precision, radius, probability) // 2)
+    bands = max(1, guess_bands(h, radius, probability) // 2)
     rows = h // bands
 
     mh = LSBMinHash(precision=precision)
@@ -73,7 +99,7 @@ def minhash(data, precision=4, key=None, radius=0.8, probability=0.9):
         if key is not None:
             k = key(item)
 
-        signature = mh.hash(k)
+        signature = mh.create_signature(k)
 
         binary = ''.join([bin(i)[2:].rjust(64, '0') for i in signature])