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Behavior of the Concept Lattice Reduction to visualizing data after Using Matrix Decompositions

机译:使用矩阵分解后概念晶格的行为降低到可视化数据

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High complexity of formal concept analysis algorithms and lattice construction algorithms are main problems today. If we want to compute all concepts from huge incidence matrix, complexity plays a great role. In some cases, we do not need to compute all concepts, but only some of them. Our research focuses on behavior of the Concept Lattice Reduction after using matrix decompositions. Modified matrix has lower dimensions and acts as input for some known algorithms for lattice construction. In this paper we want to describe the deferent between methods for matrix decompositions and describe their influence on the concept lattice.
机译:正式概念分析算法的高复杂性和格子建设算法今天是主要问题。如果我们想从巨大的发病矩阵中计算所有概念,复杂性起着很大的作用。在某些情况下,我们不需要计算所有概念,而是只有其中一些。我们的研究侧重于使用矩阵分解后概念晶格减少的行为。修改的矩阵具有较低的尺寸,并充当用于晶格结构的一些已知算法的输入。在本文中,我们希望描述矩阵分解方法之间的延期,并描述它们对概念格的影响。

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