Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139670
Title: Ultra-scalable spectral clustering and ensemble clustering
Authors: Huang, Dong
Wang, Chang-Dong
Wu, Jiansheng
Lai, Jian-Huang
Kwoh, Chee-Keong
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Huang, 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.2019.2903410
Journal: IEEE Transactions on Knowledge and Data Engineering
Series/Report no.: IEEE Transactions on Knowledge and Data Engineering
Abstract: This 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 .
URI: https://hdl.handle.net/10356/139670
ISSN: 1041-4347
DOI: 10.1109/TKDE.2019.2903410
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.2019.2903410
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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