A New Efficient Approach for Updating Formal Fuzzy Concepts
Shemis, E., Elhady, G., Mohammed, A., Keshk, A. (2023). A New Efficient Approach for Updating Formal Fuzzy Concepts. EKB Journal Management System, 10(3), 26-33. doi: 10.21608/ijci.2023.235339.1123
Ebtesam Elhossiny Shemis; Gamal Farouk Elhady; Ammar Mohammed Mohammed; Arabi Elsayed Keshk. "A New Efficient Approach for Updating Formal Fuzzy Concepts". EKB Journal Management System, 10, 3, 2023, 26-33. doi: 10.21608/ijci.2023.235339.1123
Shemis, E., Elhady, G., Mohammed, A., Keshk, A. (2023). 'A New Efficient Approach for Updating Formal Fuzzy Concepts', EKB Journal Management System, 10(3), pp. 26-33. doi: 10.21608/ijci.2023.235339.1123
Shemis, E., Elhady, G., Mohammed, A., Keshk, A. A New Efficient Approach for Updating Formal Fuzzy Concepts. EKB Journal Management System, 2023; 10(3): 26-33. doi: 10.21608/ijci.2023.235339.1123

A New Efficient Approach for Updating Formal Fuzzy Concepts

IJCI. International Journal of Computers and Information
Article 5, Volume 10, Issue 3, November 2023, Page 26-33  XML PDF (612.5 K)
Document Type: Original Article
DOI: 10.21608/ijci.2023.235339.1123
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Authors
Ebtesam Elhossiny Shemis email orcid 1; Gamal Farouk Elhady2; Ammar Mohammed Mohammedorcid 3; Arabi Elsayed Keshk4
1Computer science department, faculty of Computers and Information Systems, Menufia University, shebin El-Kom
2Faculty of Computers and Information, Menoufia University
3Computer science department, Cairo university Faculty of Graduate Studies for Statistical Research, Giza, Egypt
4Computer Science, Faculty of Computers and Information, Menoufia University
Abstract
Formal fuzzy concept analysis is an effective data analysis and mining technique in the real world. However, deriving formal fuzzy concepts is an NP problem that demands substantial time and storage resources. With the continuous exponential growth of real-world data, there is a need to regularly update the extracted list of fuzzy concepts. This research paper presents a novel and efficient algorithm to update the extracted fuzzy concepts when inserting new data objects. The proposed algorithm eliminates the need for regenerating fuzzy formal concepts by reprocessing the entire dataset. Instead, it processes only the changed part and merges it with the old list of fuzzy concepts. We have evaluated the proposed approach over various datasets of different types: quantitative, categorical, and synthesized fuzzy data. The experimental results demonstrate that the proposed algorithm outperforms the traditional approach of fuzzy concept extraction by updating only the extracted fuzzy concepts rather than recreating them from scratch, especially in the case of massive data sets.
Keywords
Fuzzy formal concept analysis; Fuzzy set theory; Dynamic fuzzy concepts; Dynamic real-world data; formal fuzzy concepts
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