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• W2069575893 abstract "During the design of concept lattices, complexity plays a major role in computing all the concepts from the huge incidence matrix. Hence for reducing the size of the lattice, methods based on matrix decompositions like SVD are available in the literature. However, SVD computation is known to have large time and memory requirements. In this paper, we propose a new method based on Fuzzy K-Means clustering for reducing the size of the concept lattices. We demonstrate the implementation of proposed method on two application areas: information retrieval and information visualization." @default.
• W2069575893 created "2016-06-24" @default.
• W2069575893 creator A5076436339 @default.
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• W2069575893 date "2010-03-15" @default.
• W2069575893 modified "2023-10-18" @default.
• W2069575893 title "Concept lattice reduction using fuzzy K-Means clustering" @default.
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• W2069575893 doi "https://doi.org/10.1016/j.eswa.2009.09.026" @default.
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