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• W3203541444 abstract "Many complicated systems can be represented by complex networks. Their accurate clustering analysis plays a critical role in understanding their intrinsic organizations. An effective Fuzzy-based Clustering Algorithm for Networks (FCAN) has thus been developed. However, its major disadvantage is its slow convergence to optimal or near-optimal solutions. To overcome this problem, we make use of a generalized momentum method to accelerate it and accordingly propose a fast fuzzy clustering algorithm, namely F <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> CAN. Experimental results on several practical datasets demonstrate that F <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> CAN performed better than FCAN in terms of efficiency while maintaining the same-level accuracy. Hence, it is more promising to conduct an accurate and fast clustering analysis for complex networks." @default.
• W3203541444 created "2021-10-11" @default.
• W3203541444 creator A5006595433 @default.
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• W3203541444 date "2021-08-23" @default.
• W3203541444 modified "2023-09-24" @default.
• W3203541444 title "Incorporating Generalized Momentum Method to Accelerate Clustering Analysis of Complex Networks" @default.
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• W3203541444 doi "https://doi.org/10.1109/case49439.2021.9551512" @default.
• W3203541444 hasPublicationYear "2021" @default.
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