  Currently, many intelligence systems contain the texts from multi-sources,
e.g., bulletin board system (BBS) posts, tweets and news. These texts can be
``comparative'' since they may be semantically correlated and thus provide us
with different perspectives toward the same topics or events. To better
organize the multi-sourced texts and obtain more comprehensive knowledge, we
propose to study the novel problem of Mutual Clustering on Comparative Texts
(MCCT), which aims to cluster the comparative texts simultaneously and
collaboratively. The MCCT problem is difficult to address because 1)
comparative texts usually present different data formats and structures and
thus they are hard to organize, and 2) there lacks an effective method to
connect the semantically correlated comparative texts to facilitate clustering
them in an unified way. To this aim, in this paper we propose a Heterogeneous
Information Network-based Text clustering framework HINT. HINT first models
multi-sourced texts (e.g. news and tweets) as heterogeneous information
networks by introducing the shared ``anchor texts'' to connect the comparative
texts. Next, two similarity matrices based on HINT as well as a transition
matrix for cross-text-source knowledge transfer are constructed. Comparative
texts clustering are then conducted by utilizing the constructed matrices.
Finally, a mutual clustering algorithm is also proposed to further unify the
separate clustering results of the comparative texts by introducing a
clustering consistency constraint. We conduct extensive experimental on three
tweets-news datasets, and the results demonstrate the effectiveness and
robustness of the proposed method in addressing the MCCT problem.
