Factor structures and central points by similarity

R. Belohlávek, M. Krupka
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引用次数: 1

Abstract

We present a general framework and results on factorization of systems of fuzzy sets by similarity. The result of such factorization can be regarded as a simplified version of the original system in which we deliberately do not distinguish elements which are highly similar. We assume that the fuzzy sets are fixed points of some closure operator. Examples of such systems are fuzzy concept lattices, fuzzy sets in a given universe, and complete residuated lattices. The similarity relation we consider is an a-cut of a particular fuzzy equivalence relation with a being a similarity threshold supplied by a user which controls the meaning of ldquohighly similarrdquo. We present results describing the factorization including an efficient way to compute the factor structure. In addition, we describe a-central points of a given collection of fixed points of a closure operator, i.e. points which are similar to every point in the collection to degree at least a.
因子结构和中心点的相似性
本文给出了模糊集系统相似性分解的一般框架和结果。这种因式分解的结果可以看作是原始系统的简化版本,在原始系统中,我们故意不区分高度相似的元素。我们假设模糊集是某个闭包算子的不动点。这类系统的例子有模糊概念格、给定宇宙中的模糊集和完全残差格。我们考虑的相似关系是一个特定模糊等价关系的一个切分,其中a是由用户提供的一个相似阈值,它控制着ldquo高度相似quo的含义。我们给出了描述因子分解的结果,包括计算因子结构的有效方法。此外,我们描述了闭包算子的不动点的给定集合的a-中心点,即与集合中的每个点相似度至少为a的点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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