QuoraAnswered6.py

from sklearn.cluster import KMeans
from sklearn import metrics
import pylab as pl
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
kmeans_model = KMeans(n_clusters= 60, init='k-means++', max_iter=100)  
X = kmeans_model.fit(model.docvecs.doctag_syn0)
labels= kmeans_model.labels_.tolist()

l = kmeans_model.fit_predict(model.docvecs.doctag_syn0)

#map each centroid to its topic tag
word_centroid_map = dict(zip( model.docvecs.offset2doctag, l))

#Print Cluster List
for cluster in range(0,100): 
    print("\nCluster %d" % cluster)
    words = []
    for i in range(0,len(word_centroid_map.values())):
        if(list(word_centroid_map.values())[i] == cluster ):
            words.append(list(word_centroid_map.keys())[i])
    print(words)