I tried running mllib k-means with 20newsgroups data set from sklearn. On a
5000 document data set I get one cluster with most of the documents and
other clusters just have handful of documents.
#code
newsgroups_train =
fetch_20newsgroups(subset='train',random_state=1,remove=('headers',
'footers', 'quotes'))
small_list = random.sample(newsgroups_train.data,5000)
def get_word_vec(text,vocabulary):
word_lst = tokenize_line(text)
word_counter = Counter(word_lst)
lst = []
for v in vocabulary:
if v in word_counter:
lst.append(word_counter[v])
else:
lst.append(0)
return lst
docsrdd = sc.parallelize(small_list)
tf = docsrdd.map(lambda x : get_word_vec(x,vocabulary))
idf = IDF().fit(tf)
tfidf = idf.transform(tf)
clusters = KMeans.train(tfidf, 20)
#documents in each cluster, using clusters.predict(x)
Counter({0: 4978, 11: 3, 9: 2, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8:
1, 10: 1, 12: 1, 13: 1, 14: 1, 15: 1, 16: 1, 17: 1, 18: 1, 19: 1})
Please Help !
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