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An efficient method to factorize fuzzy attribute-oriented concept lattices

机译:一种分解模糊的面向属性属性概念格的有效方法

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Factorization by similarity is a mathematical technique used in formal concept analysis with fuzzy attributes for reducing the complexity of different types of fuzzy concept lattices. In this paper we find the structure of the factor lattice of a fuzzy attribute oriented concept lattice, namely the intervals representing the blocks of this lattice. We provide a procedure for generating the infimum and the supremum concepts of these intervals as fixpoints of a fuzzy closure operator. This theoretical result allows to develop a more efficient algorithm for building the factor lattice of the fuzzy attribute-oriented concept lattice. A comparison between our approach and the existing algorithms is presented. (C) 2016 Elsevier B.V. All rights reserved.
机译:通过相似性进行因子分解是一种用于形式概念分析的数学技术,具有模糊属性,用于降低不同类型的模糊概念格的复杂性。在本文中,我们找到了面向模糊属性的概念格的因子格的结构,即表示该格的块的间隔。我们提供了生成这些区间的最小和最高概念作为模糊闭合算子的固定点的过程。该理论结果允许开发一种更有效的算法,用于构建面向模糊属性的概念格的因子格。提出了我们的方法与现有算法之间的比较。 (C)2016 Elsevier B.V.保留所有权利。

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