Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139670
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dc.contributor.authorHuang, Dongen_US
dc.contributor.authorWang, Chang-Dongen_US
dc.contributor.authorWu, Jianshengen_US
dc.contributor.authorLai, Jian-Huangen_US
dc.contributor.authorKwoh, Chee-Keongen_US
dc.date.accessioned2020-05-21T01:45:37Z-
dc.date.available2020-05-21T01:45:37Z-
dc.date.issued2019-
dc.identifier.citationHuang, D., Wang, C.-D., Wu, J.-S., Lai, J.-H., & Kwoh, C.-K. (2020). Ultra-scalable spectral clustering and ensemble clustering. IEEE Transactions on Knowledge and Data Engineering, 32(6), 1212-1226. doi:10.1109/TKDE.2019.2903410en_US
dc.identifier.issn1041-4347en_US
dc.identifier.urihttps://hdl.handle.net/10356/139670-
dc.description.abstractThis paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy and a fast approximation method for K -nearest representatives are proposed for the construction of a sparse affinity sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the transfer cut is then utilized to efficiently partition the graph and obtain the clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U-SPEC while maintaining high efficiency. Based on the ensemble generation via multiple U-SEPC's, a new bipartite graph is constructed between objects and base clusters and then efficiently partitioned to achieve the consensus clustering result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time and space complexity, and are capable of robustly and efficiently partitioning 10-million-level nonlinearly-separable datasets on a PC with 64 GB memory. Experiments on various large-scale datasets have demonstrated the scalability and robustness of our algorithms. The MATLAB code and experimental data are available at https://www.researchgate.net/publication/330760669 .en_US
dc.format.extent14 p.en
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineeringen_US
dc.relation.ispartofseriesIEEE Transactions on Knowledge and Data Engineeringen
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TKDE.2019.2903410en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleUltra-scalable spectral clustering and ensemble clusteringen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/TKDE.2019.2903410-
dc.description.versionAccepted versionen_US
dc.identifier.issue6en_US
dc.identifier.volume32en_US
dc.identifier.spage1212en_US
dc.identifier.epage1226en_US
dc.subject.keywordsData Clusteringen_US
dc.subject.keywordsLarge-scale Clusteringen_US
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