k-mean without label i m try to apply k-means with Python 3 to my dataset (Amazon review)  for classify similar user (from review).

I just have a TF and TF-IDF matrix (and i have a row(user) and columns(words) value in distinct csv file)


I m starting with sklearn from this sketch

https://scikit-learn.org/stable/auto_examples/text/plot_document_clustering.html#sphx-glr-auto-examples-text-plot-document-clustering-py

but in my case i don't have label or category, and i don't understand how k-means can cluster different items from this matrix TD-IDF. 


How can I group similar words from my dataset, from the matrix
without having any information?
How do I show these n-clusters in the chart?
And how do I show the similar words of this cluster, if I do not
have any information (like label or category)?


For now this is my code : 

k = 5
km = KMeans(n_clusters=k, init='k-means++', max_iter=100, n_init=5)
km.fit(Y) ##Y is my TD-IDF matrix

original_centroids = svd.inverse_transform(km.cluster_centers_)
print(original_centroids.shape) 
for i in range(original_centroids.shape[0]):
original_centroids[i] = np.array([x for x in original_centroids[i]])
svd_centroids = original_centroids.argsort()[:, ::-1]


i would like have a set like this(with similar word near)



I m very lost, because i m new in this word.
K-means does not use labels.

The example that you looked at uses labels to compare the clusters to the labels. That part obviously requires labels; but that is not part of k-means.