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Nonnegative matrix tri-factorization based high-order co-clustering and its fast implementation (2011)
| Venue: | In Proceedings of the 11th IEEE International Conference on Data Mining. ICDM ’11. IEEE Computer Society |
| Citations: | 3 - 2 self |
BibTeX
@INPROCEEDINGS{Wang11nonnegativematrix,
author = {Hua Wang and Feiping Nie and Heng Huang and Chris Ding},
title = {Nonnegative matrix tri-factorization based high-order co-clustering and its fast implementation},
booktitle = {In Proceedings of the 11th IEEE International Conference on Data Mining. ICDM ’11. IEEE Computer Society},
year = {2011}
}
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Abstract
Abstract—The fast growth of Internet and modern tech-nologies 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 new clustering techniques, called as high-order co-clustering (HOCC), to deal with the multiple types of data at the same time. A major challenge in developing HOCC methods is how to effectively make use of all available information contained in a multi-type relational data set, including both inter-type and intra-type relationships. Meanwhile, because many real world data sets are often of large sizes, clustering methods with computationally efficient solution algorithms are of great practical interest. In this paper, we first present a general HOCC framework, named as Orthogonal Nonnegative Matrix Tri-factorization (O-NMTF),
