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发布时间：2019-03-11

论文类型：会议论文

发表时间：2016-04-19

收录刊物：EI

卷号：9650

页面范围：19-30

摘要：Clustering analysis of multi-typed objects in heterogeneous information network (HINs) is an important and challenging problem. Nonnegative Matrix Tri-Factorization (NMTF) is a popular bi-clustering algorithm on document data and relational data. However, few algorithms utilize this method for clustering in HINs. In this paper, we propose a novel bi-clustering algorithm, BMFClus, for HIN based on NMTF. BMFClus not only simultaneously generates clusters for two types of objects but also takes rich heterogeneous information into account by using a similarity regularization. Experiments on both synthetic and real-world datasets demonstrate that BMFClus outperforms the stateof- the-art methods. © Springer International Publishing Switzerland 2016.