  We present Sampled Weighted Min-Hashing (SWMH), a randomized approach to
automatically mine topics from large-scale corpora. SWMH generates multiple
random partitions of the corpus vocabulary based on term co-occurrence and
agglomerates highly overlapping inter-partition cells to produce the mined
topics. While other approaches define a topic as a probabilistic distribution
over a vocabulary, SWMH topics are ordered subsets of such vocabulary.
Interestingly, the topics mined by SWMH underlie themes from the corpus at
different levels of granularity. We extensively evaluate the meaningfulness of
the mined topics both qualitatively and quantitatively on the NIPS (1.7 K
documents), 20 Newsgroups (20 K), Reuters (800 K) and Wikipedia (4 M) corpora.
Additionally, we compare the quality of SWMH with Online LDA topics for
document representation in classification.
