Fuzzy-rough set and fuzzy ID3 decision approaches to knowledge discovery in datasets

A. S. Salama, O. G. Elbarbary
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引用次数: 3

Abstract

Fuzzy rough sets are the generalization of traditional rough sets to deal with both fuzziness and vagueness in data. The existing researches on fuzzy rough sets mainly concentrate on the construction of approximation operators. Less effort has been put on the knowledge discovery in datasets with fuzzy rough sets. This paper mainly focuses on knowledge discovery in datasets with fuzzy rough sets. After analyzing the previous works on knowledge discovery with fuzzy rough sets, we introduce formal concepts of attribute reduction with fuzzy rough sets and completely study the structure of attribute reduction.
数据集中知识发现的模糊粗糙集和模糊ID3决策方法
模糊粗糙集是对传统粗糙集的推广,用于处理数据的模糊性和模糊性。现有的模糊粗糙集研究主要集中在逼近算子的构造上。对于模糊粗糙集数据集的知识发现,研究较少。本文主要研究模糊粗糙集数据集中的知识发现问题。在分析前人关于模糊粗糙集知识发现的研究成果的基础上,引入了模糊粗糙集属性约简的形式化概念,并对属性约简的结构进行了全面的研究。
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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