--- a
+++ b/clusters/scripts/3009kcenterkm.py
@@ -0,0 +1,50 @@
+from __future__ import print_function
+from sklearn.decomposition import TruncatedSVD
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.feature_extraction.text import HashingVectorizer
+from sklearn.feature_extraction.text import TfidfTransformer
+from sklearn.preprocessing import Normalizer
+from sklearn import metrics
+
+from sklearn.cluster import KMeans, MiniBatchKMeans, AffinityPropagation
+
+import logging
+import sys
+from time import time
+
+import numpy as np
+import pandas as pd
+
+data = open("clean3009kcenters.txt", 'r').readlines()
+dataset = []
+data = np.array(data)
+for x in data :
+        dataset.append(x[:len(x)-2])
+
+vectorizer = TfidfVectorizer(max_df=0.5, max_features=10000, min_df=2, stop_words='english', use_idf=True)
+X = vectorizer.fit_transform(dataset)
+print(X.shape)
+
+nb_clust = 60
+
+km = KMeans(n_clusters=nb_clust, random_state=1337, init='k-means++', max_iter=300, n_init=1,verbose=True)
+km.fit(X)
+
+f2 = open('./center3009kclusters/centers.txt', 'a')
+centers = km.cluster_centers_.argsort()[:, ::-1]
+terms = vectorizer.get_feature_names()
+for i in range(nb_clust):
+        f2.write("Cluster %d:" % i)
+        for ind in centers[i, :10]:
+                f2.write(' %s' % terms[ind])
+        f2.write("\n")
+
+cluster_map = pd.DataFrame()
+
+cluster_map['cluster'] = km.labels_
+
+for x in range(nb_clust) :
+        f1 = open('./center3009kclusters/clust_' + str(x) + '.txt', 'a')
+        y = cluster_map[cluster_map.cluster == x]['cluster'].index
+        for n in y :
+                f1.write(data[n])