The Combination of Evolutionary Algorithm Method for Numerical Association Rule Mining Optimization

被引：7

作者：

Tahyudin, Imam ^{[1
]}

Nambo, Hidetaka ^{[1
]}

机构：

[1] Kanazawa Univ, Grad Sch Nat Sci & Technol, Div Elect Engn & Comp Sci, Kanazawa, Ishikawa, Japan

来源：

PROCEEDINGS OF THE TENTH INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT | 2017年 / 502卷

关键词：

PSO; Cauchy distribution; Numerical association rule mining; Multi-objective functions; MULTIOBJECTIVE GENETIC ALGORITHMS; PARTICLE SWARM OPTIMIZATION; A-PRIORI DISCRETIZATION; STRATEGY;

D O I：

10.1007/978-981-10-1837-4_2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The numerical problem of association rule mining is an updated issue. Numerous authors propose some methods to solved it. A number of them are using the optimization approach by Particle Swarm Optimization (PSO). The problem is that the PSO trapped in local optima when searched the best particle in every iteration. Many researchers solved this problem by combining with Cauchy distribution because it is tremendous for searching in a large neighborhood. Hence, that combination will be implemented to accomplish the numerical association rule mining problem for some objective functions such as confidence, comprehensibility, interestingness. Based on the result themulti-objective of PSO forNumerical Association Rule Mining Problem with Cauchy Distribution (PARCD) showed the better result than the method of Multi-objective Particle Swarm Optimization for Association Rule Mining (MOPAR).

引用

页码：13 / 23

页数：11

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