I am trying to use k-means clustering to classify text documents. Is it possible to take a set of documents tfidf vectorize them and perform the computation then add more documents to be classified?

This is what I have so far 

```
true_k = 4
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(documents)
model = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1)
model.fit(X)

```

How would I add more documents to X? Because I would like to pickle X and save it.