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• W2109129219 abstract "To effectively exploit large-scale data sets using a limited storage space, it is necessary to find a special treatment which reduces them. There are certain methods with this intention. We can quote clustering method. However, this method proves its limits in the case of large-scale data sets. In this paper, we propose to reduce the workspace using the Principal Component Analysis (PCA). We work with fuzzy clustering of a data set in which users don't know the optimal number of clusters to be generated. We proved the effectiveness of the preprocessing use of this technique before any clustering operation." @default.
• W2109129219 created "2016-06-24" @default.
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• W2109129219 date "2011-06-01" @default.
• W2109129219 modified "2023-09-26" @default.
• W2109129219 title "Fuzzy clustering of large-scale data sets using Principal Component Analysis" @default.
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• W2109129219 doi "https://doi.org/10.1109/fuzzy.2011.6007435" @default.
• W2109129219 hasPublicationYear "2011" @default.
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