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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
When we compare these methods with the previous methods, we see that these methods are more accurate.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, we generalize the notations of rough sets based on the topological space.
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Key Findings & Data:Then we introduce new methods to find the approximations by using these generated topologies.
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Conclusion & Impact:When we compare these methods with the previous methods, we see that these methods are more accurate.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
In this paper, we generalize the notations of rough sets based on the topological space. Firstly, we produce various topologies by using the concept of ideal, $C_j$ -neighbourhoods and $P_j$ -neighbourhoods. When we compare these topologies with previous topologies, we see that these topologies are more general. Then we introduce new methods to find the approximations by using these generated topologies. When we compare these methods with the previous methods, we see that these methods are more accurate.
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Cite This Article
GÜLER, A. Ç., ÖZBAKIR, O. B., YILDIRIM, E. D. (2022). Rough approximations based on different topologies via ideals. Turkish Journal of Mathematics, Cilt: 46 - Sayı: 4, 1177–1192. https://ajindex.com/articles/607275