--- a
+++ b/clusters/scripts/clustertest.py
@@ -0,0 +1,52 @@
+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
+
+import logging
+import sys
+from time import time
+
+import numpy as np
+import pandas as pd
+
+data = pd.read_csv("cleansmplinds.csv", sep = ",", quoting = 1, quotechar = '"')
+dataset = []
+data = np.array(data)
+for x in data :
+	count = 0
+	placeholder = ""
+	for y in x :
+		if (count == 2) :
+			placeholder = y + "";
+		elif (count == 3) :
+			if y == True :
+				dataset.append(str(1) + " " + placeholder)
+			else :
+				dataset.append(str(0) + " " + placeholder)
+		count=count+1
+
+nb_clust = 100
+
+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)
+
+km = KMeans(n_clusters=nb_clust, init='k-means++', max_iter=100, n_init=1,verbose=False)
+km.fit(X)
+
+print("Top terms per cluster:")
+order_centroids = km.cluster_centers_.argsort()[:, ::-1]
+terms = vectorizer.get_feature_names()
+for i in range(nb_clust):
+    print("Cluster %d:" % i, end='')
+    for ind in order_centroids[i, :10]:
+        print(' %s' % terms[ind], end='')
+print()
+
+#use elbow method to determine number of clusters