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- W3100368668 abstract "Summary As an important data analysis method, rough set theory can be used to analyze data with uncertainties. Rough set is able to acquire knowledge by the indistinguishable relationship among data objects without any prior knowledge. Rough set theory provides a new theoretical means for solving soft computing problems and has a wide application space in data mining. Meanwhile, the fuzzy C‐means clustering algorithm is sensitive to noisy points and has low convergence speed. In order to deal with the above problems, a novel fuzzy clustering algorithm based on rough set and inhibitive factor is proposed. According to the related concepts of rough set theory, the membership model of fuzzy C‐means algorithm is redefined. Besides, an inhibitive factor is set to improve the convergence speed of the algorithm under the premise of guaranteeing the clustering effect." @default.
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- W3100368668 date "2020-11-11" @default.
- W3100368668 modified "2023-10-18" @default.
- W3100368668 title "A novel fuzzy clustering algorithm based on rough set and inhibitive factor" @default.
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- W3100368668 doi "https://doi.org/10.1002/cpe.6078" @default.
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