Abstract: In order to realize more accurate collaborative clustering by using both attribute information and structure information, we proposed a semi supervised collaborative clustering framework based on attribute heterogeneous information network (SCCAIN) at the same time. Firstly, a learnable global association measure was designed, which integrated structural association and attribute association through meta path and attribute projection. Secondly, the three factor decomposition of constraint negative matrix was introduced into the constrained collaborative clustering nodes, the correlation measurement and collaborative clustering were combined, the collaborative clustering results were taken as the sharing factor, and a unified semi supervised learning framework was proposed to jointly optimize the given constraints of collaborative clustering and correlation measurement. Finally, simulation experiments were carried out on different data sets, the experimental results show that the clustering effect of the proposed
 method is good, which verifies that the attribute information and structure information can improve the effect of collaborative clustering.

Key words: collaborative clustering, heterogeneous information network, joint optimization, association measurement