Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining

2017 ◽  
Vol 31 (8) ◽  
pp. 3559-3572 ◽  
Author(s):  
R. J. Kuo ◽  
Monalisa Gosumolo ◽  
Ferani E. Zulvia
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Multi Objective

scholarly journals Application of Weighted Particle Swarm Optimization in Association Rule Mining

2012 ◽  
pp. 279-284
Author(s):  
Manisha Gupta
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Threshold Values ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Fitness Value

Determination of the threshold values of support and confidence, affect the quality of association rule mining up to a great extent. Focus of my study is to apply weighted PSO for evaluating threshold values for support and confidence. The particle swarm optimization algorithm first searches for the optimum fitness value of each particle and then finds corresponding support and confidence as minimal threshold values after the data are transformed into binary values. The proposed method is verified by applying the Food Mart 2000 database of Microsoft SQL Server 2000. I am expecting that the particle swarm optimization algorithm will suggest suitable threshold values and obtain quality rules as per the previous works [1].

Fuzzy Multi-Objective Association Rule Mining Using Evolutionary Computation

2017 ◽  
pp. 119-148
Author(s):  
Ganghishetti Pradeep ◽  
Vadlamani Ravi
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Threshold Accepting ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Association Rule Miner

In this chapter, we model association rule mining as a Fuzzy multi-objective global optimization problem by considering several measures of strength such as support, confidence, coverage, comprehensibility, leverage, interestingness, lift and conviction by utilizing various fuzzy aggregator operators. In this, pdel, each measure has its own level of significance. Three fuzzy multi-objective association rule mining techniques viz., Fuzzy Multi-objective Binary Particle Swarm Optimization based association rule miner (FMO-BPSO), a hybridized Fuzzy Multi-objective Binary Firefly Optimization and Threshold Accepting based association rule miner (FMO-BFFOTA), hybridized Fuzzy Multi-objective Binary Particle Swarm Optimization and Threshold Accepting based association rule miner (FMO-BPSOTA) have been proposed. These three algorithms have been tested on various datasets such as book, food, bank, grocery, click stream and bakery datasets along with three fuzzy aggregate operators. From these experiments, we can conclude that Fuzzy-And outperforms all the other operators.

Sign in / Sign up

Export Citation Format

Share Document