Multi-objective optimisation based fuzzy association rule mining method

Fuzzy association rule mining (FARM) is a mainstream method to discover hidden patterns and association rules in quantitative data. It is essential to improve performance metrics, including quantity performance (e.g., the number of rules, the number of frequent itemsets) and quality performance (e.g...

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Veröffentlicht in:World wide web (Bussum) Jg. 26; H. 3; S. 1055 - 1072
Hauptverfasser: Zheng, Hui, He, Jing, Liu, Qing, Li, Jianhua, Huang, Guangli, Li, Peng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.05.2023
Springer Nature B.V
Schlagworte:
Algorithms
Blood pressure
Business metrics
Computer Science
Data mining
Database Management
Datasets
Diabetes
Fuzzy logic
Fuzzy sets
Genetic algorithms
Hypertension
Information Systems Applications (incl.Internet)
Linear programming
Methods
Multiple objective analysis
Operating Systems
Optimization
Performance enhancement
Performance measurement
Special Issue on Web Intelligence =Artificial Intelligence in the Connected World
World Wide Web
Fuzzy association rules
Optimisations
Multi-objective functions
ISSN:1386-145X, 1573-1413
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Zusammenfassung:Fuzzy association rule mining (FARM) is a mainstream method to discover hidden patterns and association rules in quantitative data. It is essential to improve performance metrics, including quantity performance (e.g., the number of rules, the number of frequent itemsets) and quality performance (e.g., fuzzy support and confidence). The current approaches inadequately support optimisation of both quantity and quality performance. We propose a multi-objective optimisation algorithm for FARM (MOOFARM), where quantity and quality performance metrics are improved and validated simultaneously. The experimental evaluation conducted on a real dataset showcases the outstanding performance of MOOFARM against state-of-the-art works. In particular, at minimum support = 0.1, minimum confidence = 0.7, our MOOFARM increases the quantity performance up to 11 times. The proposed method improves the quality performance up to 71.05 % .
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1386-145X
1573-1413
DOI:10.1007/s11280-022-01073-8