k-means

k = 2 # Define the number of clusters in which we want to partion THE data
# Define the proper notion of distance to deal with documents
from sklearn.metrics.pairwise import cosine_similarity
dist = 1 - cosine_similarity(X)
# Run the algorithm KMeans
model = KMeans(n_clusters = k)
model.fit(X);

print("Top terms per cluster:\n")
order_centroids = model.cluster_centers_.argsort()[:, ::-1]
terms = vectorizer.get_feature_names()
for i in range(k):
    print ("Cluster %i:" % i, end='')
    for ind in order_centroids[i, :3]:
        print (' %s,' % terms[ind], end='')
    print ("")
Top terms per cluster:

Cluster 0: awesome, staff, cs50,
Cluster 1: dog, cat, keeps,