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 set two objects have the same attribute values, then these two objects are called indiscernible. This indiscernibility has an effect on the membership relat...
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| Main Authors: | |
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| Corporate Author: | |
| Format: | Article |
| Published: |
University of Szeged, Institute of Informatics
Szeged
2019
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| Series: | Acta cybernetica
24 No. 1 |
| Kulcsszavak: | Számítástechnika, Algoritmus, Programozás |
| Subjects: | |
| doi: | 10.14232/actacyb.24.1.2019.8 |
| Online Access: | http://acta.bibl.u-szeged.hu/59230 |
| Summary: | 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 set two 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 needs to be stated, then all the objects that are indiscernible from the given object must be taken into consideration. The indiscernibility relation is an equivalence relation which represents the 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, (see in [10, 11, 9]) the possible usage of correlation clustering in rough set theory was investigated. In this paper, the authors show how different types of search algorithms can affect the set approximation. |
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| Physical Description: | 105-120 |
| ISSN: | 0324-721X |