Release Time:2019-03-11  Hits:

Indexed by: Conference Paper

Date of Publication: 2016-04-19

Included Journals: EI

Volume: 9650

Page Number: 19-30

Abstract: 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.