Efficient Parallel Algorithm for Extracting Fuzzy-Crisp Formal Concepts

Fuzzy Formal Concept Analysis (FFCA) is a robust mathematical tool for analyzing data, particularly where uncertainty or fuzziness is inherent. FFCA is utilized across various domains, including data mining, information retrieval, and knowledge representation. However, fuzzy concepts extraction is a...

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Veröffentlicht in:International journal of advanced computer science & applications Jg. 15; H. 8
Hauptverfasser: Shemis, Ebtesam, Keshk, Arabi, Mohammed, Ammar, Elhady, Gamal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: West Yorkshire Science and Information (SAI) Organization Limited 2024
Schlagworte:
Algorithms
Computational efficiency
Computing time
Data analysis
Data mining
Datasets
Fuzzy sets
Information retrieval
Knowledge representation
Resource utilization
Uncertainty analysis
ISSN:2158-107X, 2156-5570
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Zusammenfassung:Fuzzy Formal Concept Analysis (FFCA) is a robust mathematical tool for analyzing data, particularly where uncertainty or fuzziness is inherent. FFCA is utilized across various domains, including data mining, information retrieval, and knowledge representation. However, fuzzy concepts extraction is a crucial yet computationally intensive task. This paper addresses the challenge of time efficiency in extracting single-sided fuzzy concepts from large datasets. A parallel algorithm is proposed to reduce computational time and optimize resource utilization, thus enabling the scalable analysis of expanding datasets. By computing fuzzy concepts across multiple threads in parallel, each thread processes an attribute independently to extract fuzzy concepts, which are then merged in the final step. The proposed algorithm extracts fuzzy-crisp concepts, which are more concise than other types of fuzzy concepts. Experiments were conducted to evaluate the performance of the proposed parallel algorithm against existing sequential methods. Experimental results demonstrate significant gains in computational efficiency, with the algorithm achieving an average time reduction of 68% compared to the attribute-based algorithm and up to 83%-time reduction compared to the fuzzy CbO algorithm across various types of datasets, including binary, quantitative, and fuzzy.
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.0150862