Simultaneous Clustering of Multi-Type Relational Data via Symmetric Nonnegative Matrix Tri-factorization
Hua Wang, Heng Huang, Chris Ding.
CIKM - 2011
The rapid growth of Internet and modern technologies has brought data involving objects of multiple types that are related to each other, called as multi-type relational data. Traditional clustering methods for single-type data rarely work well on them, which calls for more advanced clustering techniques to deal with multiple types of data simultaneously to utilize their interrelatedness. A major challenge in developing simultaneous clustering methods is how to effectively use all available information contained in a multi-type relational data set including inter-type and intra-type relationships. In this paper, we propose a Symmetric Nonnegative Matrix Tri-Factorization (S-NMTF) framework to cluster multi-type relational data at the same time. The proposed S-NMTF approach employs NMTF to simultaneously cluster different types of data using their inter-type relationships, and incorporate the intra-type information through manifold regularization. In order to deal with the symmetric usage of the factor matrix in S-NMTF, we present a new generic matrix inequality to derive the solution algorithm, which involves a fourth-order matrix polynomial, in a principled way. Promising experimental results have validated the proposed approach.
Links
- View publications from Hua Wang
- View publications presented in CIKM
- View publications researching Matrix/Tensor Factorization
- View publications applied to Natural Language Processing
Cite this paper
MLA
Wang, Hua, Heng Huang, and Chris Ding. "Simultaneous clustering of multi-type relational data via symmetric nonnegative matrix tri-factorization." Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, 2011.
BibTeX
@inproceedings{wang2011simultaneous,
title={Simultaneous clustering of multi-type relational data via symmetric nonnegative matrix tri-factorization},
author={Wang, Hua and Huang, Heng and Ding, Chris},
booktitle={Proceedings of the 20th ACM international conference on Information and knowledge management},
pages={279--284},
year={2011},
organization={ACM}
}