Type
ArticleKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab
Date
2021-06-02Online Publication Date
2021-06-02Print Publication Date
2021-06Embargo End Date
2022-06-02Submitted Date
2020-11-21Permanent link to this record
http://hdl.handle.net/10754/669566
Metadata
Show full item recordAbstract
Traditional clustering algorithms focus on a single clustering result; as such, they cannot explore potential diverse patterns of complex real world data. To deal with this problem, approaches that exploit meaningful alternative clusterings in data have been developed in recent years. Existing algorithms, including single view/multi-view multiple clustering methods, are designed for applications with i.i.d. data samples, and cannot handle the data samples with dependency presented in networks, especially in heterogeneous information networks (HIN). In this paper, we propose a framework (NetMCs) that can explore multiple clusterings in HIN. Specifically, NetMCs adopts a set of meta-path schemes with different semantics on HIN, and considers each meta-path scheme as a base clustering aspect. Guided by the meta-path schemes, NetMCs then introduces a variation of the skip-gram framework that can jointly optimize multiple clustering aspects, and simultaneously obtain the respective embedding representations and individual clusterings therein. To reduce redundancy between alternative clusterings, NetMCs utilizes an explicit regularization term to control the embedding diversity of the same nodes among different clustering aspects. Experiments on benchmark HIN datasets confirm the performance of NetMCs in generating multiple clusterings with high quality and diversity.Citation
Wei, S., Yu, G., Wang, J., Domeniconi, C., & Zhang, X. (2021). Multiple clusterings of heterogeneous information networks. Machine Learning. doi:10.1007/s10994-021-06000-ySponsors
This work is partially supported by NSFC (Nos. 62031003, 62072380 and 61872300).Publisher
Springer Science and Business Media LLCJournal
Machine LearningAdditional Links
https://link.springer.com/10.1007/s10994-021-06000-yae974a485f413a2113503eed53cd6c53
10.1007/s10994-021-06000-y