A new evolutionary optimization based on multi-objective firefly algorithm for mining numerical association rules

Association rule mining (ARM) is a widely used technique in data mining for pattern discovery. However, association rule mining in numerical data poses a considerable challenge. In recent years, researchers have turned to optimization-based approaches as a potential solution. One particular area of...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 28; no. 9-10; pp. 6879 - 6892
Main Authors: Rokh, Babak, Mirvaziri, Hamid, Olyaee, MohammadHossein
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024
Springer Nature B.V
Subjects:
Application of Soft Computing
Artificial Intelligence
Computational Intelligence
Control
Data mining
Engineering
Evolutionary algorithms
Genetic algorithms
Heuristic methods
Intervals
Knowledge discovery
Mathematical Logic and Foundations
Mechatronics
Multiple objective analysis
Objectives
Optimization
Researchers
Robotics
Numerical association rule mining
Data mining
Multi-objective evolutionary algorithm
Firefly algorithm
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:Association rule mining (ARM) is a widely used technique in data mining for pattern discovery. However, association rule mining in numerical data poses a considerable challenge. In recent years, researchers have turned to optimization-based approaches as a potential solution. One particular area of interest in numerical association rules mining (NARM) is controlling the length of itemset intervals. In this paper, we propose a novel evolutionary algorithm based on the multi-objective firefly algorithm for efficiently mining numerical association rules (MOFNAR). MOFNAR utilizes Balance, square of cosine (SOC) and comprehensibility as objectives of evolutionary algorithm to assess rules and achieve a rule set that is both simple and accurate. We introduce the Balance measure to effectively control the intervals of numerical itemsets and eliminate misleading rules. Furthermore, we suggest a penalty approach, and the crowding-distance method is employed to maintain high diversity. Experimental results on five well-known datasets show the effectiveness of our method in discovering a simple rule set with high confidence that covers a significant percentage of the data.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-09558-y