Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28609
Title: Fast graph-based relaxed clustering for large data sets using minimal enclosing ball
Authors: Qian, P
Chung, FL 
Wang, S
Deng, Z
Keywords: Clustering
Large data sets
Minimal enclosing ball (MEB)
Time complexity
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, 2012, v. 42, no. 3, 6145713, p. 672-687 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics 
Abstract: Although graph-based relaxed clustering (GRC) is one of the spectral clustering algorithms with straightforwardness and self-adaptability, it is sensitive to the parameters of the adopted similarity measure and also has high time complexity O(N 3) which severely weakens its usefulness for large data sets. In order to overcome these shortcomings, after introducing certain constraints for GRC, an enhanced version of GRC [constrained GRC (CGRC)] is proposed to increase the robustness of GRC to the parameters of the adopted similarity measure, and accordingly, a novel algorithm called fast GRC (FGRC) based on CGRC is developed in this paper by using the core-set-based minimal enclosing ball approximation. A distinctive advantage of FGRC is that its asymptotic time complexity is linear with the data set size N. At the same time, FGRC also inherits the straightforwardness and self-adaptability from GRC, making the proposed FGRC a fast and effective clustering algorithm for large data sets. The advantages of FGRC are validated by various benchmarking and real data sets.
URI: http://hdl.handle.net/10397/28609
ISSN: 1083-4419
DOI: 10.1109/TSMCB.2011.2172604
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