print ("Clustering result by K-means")
# km.cluster_centers_ denotes the importances of each items in centroid.
# We need to sort it in decreasing-order and get the top k items.
order_centroids = km.cluster_centers_.argsort()[:, ::-1]

Cluster_keywords_summary = {}
for i in range(num_clusters):
    print ("Cluster " + str(i) + " words: ", end='')
    Cluster_keywords_summary[i] = []
    for ind in order_centroids[i, :5]: #replace 5 with n words per cluster
        Cluster_keywords_summary[i].append(vocab_frame_dict[tf_selected_words[ind]])
        print (vocab_frame_dict[tf_selected_words[ind]] + ",", end='')

    cluster_NBA = frame.loc[i]['Name'].values
    print("\n", ", ".join(cluster_NBA), "\n")