Research on fruit shape database mining to support fruit class classification using the shuffled frog leaping optimization (SFLO) technique

Ha Huy Cuong Nguyen; Ho Phan Hieu; Chiranjibe Jana; Tran Anh Kiet; Thanh Thuy Nguyen; Ha Huy Cuong Nguyen; Ho Phan Hieu; Chiranjibe Jana; Tran Anh Kiet; Thanh Thuy Nguyen

doi:10.3934/math.2024950

AIMS Mathematics

2024, Volume 9, Issue 7: 19495-19514. doi: 10.3934/math.2024950

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Research article

Research on fruit shape database mining to support fruit class classification using the shuffled frog leaping optimization (SFLO) technique

1.
Software Development Centre-The University of Danang, Danang, 50000 Da Nang, Vietnam
2.
The University of Danang, Danang, 50000 Da Nang, Vietnam
3.
Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai 602105, Tamil Nadu, India
4.
Faculty of Computer Science, VNU University of Engineering and Technology, Hanoi, Vietnam

Received: 04 March 2024 Revised: 20 May 2024 Accepted: 19 May 2024 Published: 13 June 2024
MSC : 46N10, 47N10, 52B55

Association rule mining (ARM) is a technique for discovering meaningful associations within databases, typically handling discrete and categorical data. Recent advancements in ARM have concentrated on refining calculations to reveal connections among various databases. The integration of shuffled frog leaping optimization (SFLO) processes has played a crucial role in this pursuit. This paper introduces an innovative SFLO-based method for performance analysis. To generate association rules, we utilize the apriori algorithm and incorporate frog encoding within the SFLO method. A key advantage of this approach is its one-time database filtering, significantly boosting efficiency in terms of CPU time and memory usage. Furthermore, we enhance the optimization process's efficacy and precision by employing multiple measures with the modified SFLO techniques for mining such information.The proposed approach, implemented using MongoDB, underscores that our performance analysis yields notably superior outcomes compared to alternative methods. This research holds implications for fruit shape database mining, providing robust support for fruit class classification.
- apriori algorithm,
- association rule mining,
- shuffled frog leaping optimization,
- support,
- confidence
Citation: Ha Huy Cuong Nguyen, Ho Phan Hieu, Chiranjibe Jana, Tran Anh Kiet, Thanh Thuy Nguyen. Research on fruit shape database mining to support fruit class classification using the shuffled frog leaping optimization (SFLO) technique[J]. AIMS Mathematics, 2024, 9(7): 19495-19514. doi: 10.3934/math.2024950

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Abstract

Association rule mining (ARM) is a technique for discovering meaningful associations within databases, typically handling discrete and categorical data. Recent advancements in ARM have concentrated on refining calculations to reveal connections among various databases. The integration of shuffled frog leaping optimization (SFLO) processes has played a crucial role in this pursuit. This paper introduces an innovative SFLO-based method for performance analysis. To generate association rules, we utilize the apriori algorithm and incorporate frog encoding within the SFLO method. A key advantage of this approach is its one-time database filtering, significantly boosting efficiency in terms of CPU time and memory usage. Furthermore, we enhance the optimization process's efficacy and precision by employing multiple measures with the modified SFLO techniques for mining such information.The proposed approach, implemented using MongoDB, underscores that our performance analysis yields notably superior outcomes compared to alternative methods. This research holds implications for fruit shape database mining, providing robust support for fruit class classification.

References

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