Multi-objective Boolean grey wolf optimization based decomposition algorithm for high-frequency and high-utility itemset mining

N. Pazhaniraja; Shakila Basheer; Kalaipriyan Thirugnanasambandam; Rajakumar Ramalingam; Mamoon Rashid; J. Kalaivani; N. Pazhaniraja; Shakila Basheer; Kalaipriyan Thirugnanasambandam; Rajakumar Ramalingam; Mamoon Rashid; J. Kalaivani

doi:10.3934/math.2023920

AIMS Mathematics

2023, Volume 8, Issue 8: 18111-18140. doi: 10.3934/math.2023920

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Multi-objective Boolean grey wolf optimization based decomposition algorithm for high-frequency and high-utility itemset mining

1.
Department of Artificial Intelligence and Machine Learning, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India
2.
Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
3.
Centre for Smart Grid Technologies, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
4.
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
5.
Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, 411048, India
6.
Department of Computing Technologies, SRMIST, Kattankulathur, Chennai, India

Received: 18 January 2023 Revised: 14 April 2023 Accepted: 26 April 2023 Published: 25 May 2023
MSC : 05C85, 78M50

In itemset mining, the two vital goals that must be resolved from a multi-objective perspective are frequency and utility. To effectively address the issue, researchers have placed a great deal of emphasis on achieving both objectives without sacrificing the quality of the solution. In this work, an effective itemset mining method was formulated for high-frequency and high-utility itemset mining (HFUI) in a transaction database. The problem of HFUI is modeled mathematically as a multi-objective issue to handle it with the aid of a modified bio-inspired multi-objective algorithm, namely, the multi-objective Boolean grey wolf optimization based decomposition algorithm. This algorithm is an enhanced version of the Boolean grey wolf optimization algorithm (BGWO) for handling multi-objective itemset mining problem using decomposition factor. In the further part of this paper decomposition factor will be mentioned as decomposition. Different population initialization strategies were used to test the impact of the proposed algorithm. The system was evaluated with 12 different real-time datasets, and the results were compared with seven different recent existing multi-objective models. Statistical analysis, namely, the Wilcoxon signed rank test, was also utilized to prove the impact of the proposed algorithm. The outcome shows the impact of the formulated technique model over other standard techniques.
- Boolean operators,
- Grey Wolf Optimization,
- Frequency and Utility mining,
- Swarm intelligence,
- multi-objective algorithm
Citation: N. Pazhaniraja, Shakila Basheer, Kalaipriyan Thirugnanasambandam, Rajakumar Ramalingam, Mamoon Rashid, J. Kalaivani. Multi-objective Boolean grey wolf optimization based decomposition algorithm for high-frequency and high-utility itemset mining[J]. AIMS Mathematics, 2023, 8(8): 18111-18140. doi: 10.3934/math.2023920

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Abstract

In itemset mining, the two vital goals that must be resolved from a multi-objective perspective are frequency and utility. To effectively address the issue, researchers have placed a great deal of emphasis on achieving both objectives without sacrificing the quality of the solution. In this work, an effective itemset mining method was formulated for high-frequency and high-utility itemset mining (HFUI) in a transaction database. The problem of HFUI is modeled mathematically as a multi-objective issue to handle it with the aid of a modified bio-inspired multi-objective algorithm, namely, the multi-objective Boolean grey wolf optimization based decomposition algorithm. This algorithm is an enhanced version of the Boolean grey wolf optimization algorithm (BGWO) for handling multi-objective itemset mining problem using decomposition factor. In the further part of this paper decomposition factor will be mentioned as decomposition. Different population initialization strategies were used to test the impact of the proposed algorithm. The system was evaluated with 12 different real-time datasets, and the results were compared with seven different recent existing multi-objective models. Statistical analysis, namely, the Wilcoxon signed rank test, was also utilized to prove the impact of the proposed algorithm. The outcome shows the impact of the formulated technique model over other standard techniques.

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