属性异构信息网络的半监督协同聚类

为实现同时利用属性信息和结构信息完成更精确的协同聚类, 提出一种基于属性异构信息网络的半监督协同聚类框架（SCCAIN）. 首先, 设计一种可学习的整体关联度量, 其通过元路径和属性投影整合结构关联和属性关联； 其次, 将约束负矩阵三因式分解引入到具有约束的协同聚类节点中, 将相关性度量和协同聚类相结合, 以协同聚类结果作为共享因子, 并提出一个统一的半监督学习框架, 以联合优化协同聚类和相关性测量给定的约束; 最后, 在不同的数据集上进行仿真实验, 实验结果表明, 该方法聚类效果较好, 从而验证了属性信息和结构信息对能提升协同聚类效果.

Semi Supervised Collaborative Clustering for Attribute Heterogeneous Information Networks

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.