def perform_cluster(data, params):
km = KMeans()
km.set_params(**params)
vectorizer = TfidfVectorizer()
print(data[1][0])
tfidf = vectorizer.fit_transform(data[1])
labels = km.fit_predict(tfidf)
result = {i: [] for i in set(labels)}
for i, l in zip(range(len(labels)), labels):
result[l].append(data[0][i])
return result
