不同类型的粗糙集搜索算法

Dávid Nagy, Tamás Mihálydeák, László Aszalós
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引用次数: 2

摘要

根据现有的信息,在许多情况下,可能会发生无法区分两个对象的情况。如果给定一组数据,其中的两个对象具有相同的属性值,则这两个对象称为不可分辨的。这种不可分辨性对隶属关系有影响,因为在某些情况下,它使我们对给定对象的判断不确定。不确定性之所以出现,是因为如果需要描述一个物体的某些东西,那么必须考虑所有与给定物体不可分辨的物体。不可分辨关系是一种等价关系,它表示嵌入在信息系统中的背景知识。在Pawlakian系统中,这种关系用于集合逼近。关联聚类是一种生成分区的聚类技术。作者在前人的研究中探讨了相关聚类在粗糙集理论中的可能应用。在本文中,作者展示了不同类型的搜索算法如何影响集合逼近。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Different Types of Search Algorithms for Rough Sets
Based on the available information in many cases it can happen that two objects cannot be distinguished. If a set of data is given and in this settwo objects have the same attribute values, then these two objects are called indiscernible. This indiscernibility has an effect on the membership relation,because in some cases it makes our judgment uncertain about a given object. The uncertainty appears because if something about an object is needed to bestated, then all the objects that are indiscernible from the given object must be taken into consideration. The indiscernibility relation is an equivalencerelation which represents background knowledge embedded in an information system. In a Pawlakian system this relation is used in set approximation.Correlation clustering is a clustering technique which generates a partition. In the authors’ previous research the possible usage of the correlation clusteringin rough set theory was investigated. In this paper the authors show how different types of search algorithms affect the set approximation.
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